Exploring Music Contents - CMMR 2010

Lecture Notes in Computer Science Commenced Publication in 1973 Founding and Former Series Editors: Gerhard Goos, Juris...

Author: Solvi Ystad | Mitsuko Aramaki | Richard Kronland-Martinet | Kristoffer Jensen

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Lecture Notes in Computer Science Commenced Publication in 1973 Founding and Former Series Editors: Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen

Editorial Board David Hutchison Lancaster University, UK Takeo Kanade Carnegie Mellon University, Pittsburgh, PA, USA Josef Kittler University of Surrey, Guildford, UK Jon M. Kleinberg Cornell University, Ithaca, NY, USA Alfred Kobsa University of California, Irvine, CA, USA Friedemann Mattern ETH Zurich, Switzerland John C. Mitchell Stanford University, CA, USA Moni Naor Weizmann Institute of Science, Rehovot, Israel Oscar Nierstrasz University of Bern, Switzerland C. Pandu Rangan Indian Institute of Technology, Madras, India Bernhard Steffen TU Dortmund University, Germany Madhu Sudan Microsoft Research, Cambridge, MA, USA Demetri Terzopoulos University of California, Los Angeles, CA, USA Doug Tygar University of California, Berkeley, CA, USA Gerhard Weikum Max Planck Institute for Informatics, Saarbruecken, Germany

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Sølvi Ystad Mitsuko Aramaki Richard Kronland-Martinet Kristoffer Jensen (Eds.)

Exploring Music Contents 7th International Symposium, CMMR 2010 Málaga, Spain, June 21-24, 2010 Revised Papers

13

Volume Editors Sølvi Ystad CNRS-LMA, 31 Chemin Joseph Aiguier, 13402 Marseille Cedex 20, France E-mail: [email protected] Mitsuko Aramaki CNRS-INCM, 31 Chemin Joseph Aiguier, 13402 Marseille Cedex 20, France E-mail: [email protected] Richard Kronland-Martinet CNRS-LMA, 31 Chemin Joseph Aiguier, 13402 Marseille Cedex 20, France E-mail: [email protected] Kristoffer Jensen Aalborg University Esbjerg, Niels Bohr Vej 8, 6700 Esbjerg, Denmark E-mail: [email protected]

ISSN 0302-9743 e-ISSN 1611-3349 e-ISBN 978-3-642-23126-1 ISBN 978-3-642-23125-4 DOI 10.1007/978-3-642-23126-1 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2011936382 CR Subject Classiﬁcation (1998): J.5, H.5, C.3, H.5.5, G.3, I.5 LNCS Sublibrary: SL 3 – Information Systems and Application, incl. Internet/Web and HCI

© Springer-Verlag Berlin Heidelberg 2011 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, speciﬁcally the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting, reproduction on microﬁlms or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a speciﬁc statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Typesetting: Camera-ready by author, data conversion by Scientiﬁc Publishing Services, Chennai, India Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

Preface

Computer Music Modeling and Retrieval (CMMR) 2010 was the seventh event of this international conference series that was initiated in 2003. Since its start, the conference has been co-organized by the University of Aalborg, Esbjerg, Denmark (http://www.aaue.dk) and the Laboratoire de M´ecanique et d’Acoustique in Marseille, France (http://www.lma.cnrs-mrs.fr) and has taken place in France, Italy and Denmark. The six previous editions of CMMR oﬀered a varied overview of recent music information retrieval (MIR) and sound modeling activities in addition to alternative ﬁelds related to human interaction, perception and cognition. This year’s CMMR took place in M´ alaga, Spain, June 21–24, 2010. The conference was organized by the Application of Information and Communications Technologies Group (ATIC) of the University of M´alaga (Spain), together with LMA and INCM (CNRS, France) and AAUE (Denmark). The conference featured three prominent keynote speakers working in the MIR area, and the program of CMMR 2010 included in addition paper sessions, panel discussions, posters and demos. The proceedings of the previous CMMR conferences were published in the Lecture Notes in Computer Science series (LNCS 2771, LNCS 3310, LNCS 3902, LNCS 4969, LNCS 5493 and LNCS 5954), and the present edition follows the lineage of the previous ones, including a collection of 22 papers within the topics of CMMR. These articles were specially reviewed and corrected for this proceedings volume. The current book is divided into ﬁve main chapters that reﬂect the present challenges within the ﬁeld of computer music modeling and retrieval. The chapters span topics from music interaction, composition tools and sound source separation to data mining and music libraries. One chapter is also dedicated to perceptual and cognitive aspects that are currently the subject of increased interest in the MIR community. We are conﬁdent that CMMR 2010 brought forward the research in these important areas. We would like to thank Isabel Barbancho and her team at the Application of Information and Communications Technologies Group (ATIC) of the University of M´ alaga (Spain) for hosting the 7th CMMR conference and for ensuring a successful organization of both scientiﬁc and social matters. We would also like to thank the Program Committee members for their valuable paper reports and thank all the participants who made CMMR 2010 a fruitful and convivial event. Finally, we would like to thank Springer for accepting to publish the CMMR 2010 proceedings in their LNCS series. March 2011

Sølvi Ystad Mitsuko Aramaki Richard Kronland-Martinet Kristoﬀer Jensen

Organization

The 7th International Symposium on Computer Music Modeling and Retrieval (CMMR2010) was co-organized by the University of M´ alaga (Spain) Aalborg University (Esbjerg, Denmark), and LMA/INCM-CNRS (Marseille, France). Symposium Chair Isabel Barbancho

University of M´ alaga, Spain

Symposium Co-chairs Kristoﬀer Jensen Sølvi Ystad

AAUE, Denmark CNRS-LMA, France

Demonstration and Panel Chairs Ana M. Barbancho University of M´ alaga, Spain Lorenzo J. Tard´ on University of M´ alaga, Spain

Program Committee Paper and Program Chairs Mitsuko Aramaki Richard Kronland-Martinet

CNRS-INCM, France CNRS-LMA, France

CMMR 2010 Referees Mitsuko Aramaki Federico Avanzini Rolf Bader Isabel Barbancho Ana M. Barbancho Mathieu Barthet Antonio Camurri Laurent Daudet Olivier Derrien Simon Dixon Barry Eaglestone Gianpaolo Evangelista C´edric F´evotte Bruno Giordano Emilia G´ omez

Brian Gygi Goﬀredo Haus Kristoﬀer Jensen Anssi Klapuri Richard Kronland-Martinet Marc Leman Sylvain Marchand Gr´egory Pallone Andreas Rauber David Sharp Bob L. Sturm Lorenzo J. Tard´ on Vesa V¨alim¨ aki Sølvi Ystad

Table of Contents

Part I: Music Production, Interaction and Composition Tools Probabilistic and Logic-Based Modelling of Harmony . . . . . . . . . . . . . . . . . Simon Dixon, Matthias Mauch, and Am´elie Anglade

1

Interactive Music Applications and Standards . . . . . . . . . . . . . . . . . . . . . . . Rebecca Stewart, Panos Kudumakis, and Mark Sandler

20

Interactive Music with Active Audio CDs . . . . . . . . . . . . . . . . . . . . . . . . . . . Sylvain Marchand, Boris Mansencal, and Laurent Girin

31

Pitch Gestures in Generative Modeling of Music . . . . . . . . . . . . . . . . . . . . . Kristoﬀer Jensen

51

Part II: Music Structure Analysis - Sound Source Separation An Entropy Based Method for Local Time-Adaptation of the Spectrogram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marco Liuni, Axel R¨ obel, Marco Romito, and Xavier Rodet

60

Transcription of Musical Audio Using Poisson Point Processes and Sequential MCMC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pete Bunch and Simon Godsill

76

Single Channel Music Sound Separation Based on Spectrogram Decomposition and Note Classiﬁcation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wenwu Wang and Haﬁz Mustafa

84

Notes on Nonnegative Tensor Factorization of the Spectrogram for Audio Source Separation: Statistical Insights and towards Self-Clustering of the Spatial Cues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C´edric F´evotte and Alexey Ozerov

102

Part III: Auditory Perception, Artiﬁcial Intelligence and Cognition What Signal Processing Can Do for the Music . . . . . . . . . . . . . . . . . . . . . . . Isabel Barbancho, Lorenzo J. Tard´ on, Ana M. Barbancho, Andr´es Ortiz, Simone Sammartino, and Cristina de la Bandera

116

VIII

Table of Contents

Speech/Music Discrimination in Audio Podcast Using Structural Segmentation and Timbre Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mathieu Barthet, Steven Hargreaves, and Mark Sandler Computer Music Cloud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jes´ us L. Alvaro and Beatriz Barros Abstract Sounds and Their Applications in Audio and Perception Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Adrien Merer, Sølvi Ystad, Richard Kronland-Martinet, and Mitsuko Aramaki

138

163

176

Part IV: Analysis and Data Mining Pattern Induction and Matching in Music Signals . . . . . . . . . . . . . . . . . . . . Anssi Klapuri Unsupervised Analysis and Generation of Audio Percussion Sequences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marco Marchini and Hendrik Purwins

188

205

Identifying Attack Articulations in Classical Guitar . . . . . . . . . . . . . . . . . . ¨ Tan Hakan Ozaslan, Enric Guaus, Eric Palacios, and Josep Lluis Arcos

219

Comparing Approaches to the Similarity of Musical Chord Sequences . . . W. Bas de Haas, Matthias Robine, Pierre Hanna, Remco C. Veltkamp, and Frans Wiering

242

Part V: MIR - Music Libraries Songs2See and GlobalMusic2One: Two Applied Research Projects in Music Information Retrieval at Fraunhofer IDMT . . . . . . . . . . . . . . . . . . . . Christian Dittmar, Holger Großmann, Estefan´ıa Cano, Sascha Grollmisch, Hanna Lukashevich, and Jakob Abeßer MusicGalaxy: A Multi-focus Zoomable Interface for Multi-facet Exploration of Music Collections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sebastian Stober and Andreas N¨ urnberger A Database Approach to Symbolic Music Content Management . . . . . . . . Philippe Rigaux and Zoe Faget Error-Tolerant Content-Based Music-Retrieval with Mathematical Morphology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mikko Karvonen, Mika Laitinen, Kjell Lemstr¨ om, and Juho Vikman

259

273

303

321

Table of Contents

IX

Melodic Similarity through Shape Similarity . . . . . . . . . . . . . . . . . . . . . . . . . Juli´ an Urbano, Juan Llor´ens, Jorge Morato, and Sonia S´ anchez-Cuadrado

338

Content-Based Music Discovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dirk Sch¨ onfuß

356

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

361

Probabilistic and Logic-Based Modelling of Harmony Simon Dixon, Matthias Mauch, and Am´elie Anglade Centre for Digital Music, Queen Mary University of London, Mile End Rd, London E1 4NS, UK [email protected] http://www.eecs.qmul.ac.uk/~simond Abstract. Many computational models of music fail to capture essential aspects of the high-level musical structure and context, and this limits their usefulness, particularly for musically informed users. We describe two recent approaches to modelling musical harmony, using a probabilistic and a logic-based framework respectively, which attempt to reduce the gap between computational models and human understanding of music. The ﬁrst is a chord transcription system which uses a high-level model of musical context in which chord, key, metrical position, bass note, chroma features and repetition structure are integrated in a Bayesian framework, achieving state-of-the-art performance. The second approach uses inductive logic programming to learn logical descriptions of harmonic sequences which characterise particular styles or genres. Each approach brings us one step closer to modelling music in the way it is conceptualised by musicians. Keywords: Chord transcription, inductive logic programming, musical harmony.

1

Introduction

Music is a complex phenomenon. Although music is described as a “universal language”, when viewed as a paradigm for communication it is diﬃcult to ﬁnd agreement on what constitutes a musical message (is it the composition or the performance?), let alone the meaning of such a message. Human understanding of music is at best incomplete, yet there is a vast body of knowledge and practice regarding how music is composed, performed, recorded, reproduced and analysed in ways that are appreciated in particular cultures and settings. It is the computational modelling of this “common practice” (rather than philosophical questions regarding the nature of music) which we address in this paper. In particular, we investigate harmony, which exists alongside melody, rhythm and timbre as one of the fundamental attributes of Western tonal music. Our starting point in this paper is the observation that many of the computational models used in the music information retrieval and computer music research communities fail to capture much of what is understood about music. S. Ystad et al. (Eds.): CMMR 2010, LNCS 6684, pp. 1–19, 2011. c Springer-Verlag Berlin Heidelberg 2011

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Two examples are the bag-of-frames approach to music similarity [5], and the periodicity pattern approach to rhythm analysis [13], which are both independent of the order of musical notes, whereas temporal order is an essential feature of melody, rhythm and harmonic progression. Perhaps surprisingly, much progress has been made in music informatics in recent years1, despite the naivete of the musical models used and the claims that some tasks have reached a “glass ceiling” [6]. The continuing progress can be explained in terms of a combination of factors: the high level of redundancy in music, the simplicity of many of the tasks which are attempted, and the limited scope of the algorithms which are developed. In this regard we agree with [14], who review the ﬁrst 10 years of ISMIR conferences and list some challenges which the community “has not fully engaged with before”. One of these challenges is to “dig deeper into the music itself”, which would enable researchers to address more musically complex tasks; another is to “expand ... musical horizons”, that is, broaden the scope of MIR systems. In this paper we present two approaches to modelling musical harmony, aiming at capturing the type of musical knowledge and reasoning a musician might use in performing similar tasks. The ﬁrst task we address is that of chord transcription from audio recordings. We present a system which uses a high-level model of musical context in which chord, key, metrical position, bass note, chroma features and repetition structure are integrated in a Bayesian framework, and generates the content of a “lead-sheet” containing the sequence of chord symbols, including their bass notes and metrical positions, and the key signature and any modulations over time. This system achieves state-of-the-art performance, being rated ﬁrst in its category in the 2009 and 2010 MIREX evaluations. The second task to which we direct our attention is the machine learning of logical descriptions of harmonic sequences in order to characterise particular styles or genres. For this work we use inductive logic programming to obtain representations such as decision trees which can be used to classify unseen examples or provide insight into the characteristics of a data corpus. Computational models of harmony are important for many application areas of music informatics, as well as for music psychology and musicology itself. For example, a harmony model is a necessary component of intelligent music notation software, for determining the correct key signature and pitch spelling of accidentals where music is obtained from digital keyboards or MIDI ﬁles. Likewise processes such as automatic transcription are beneﬁtted by tracking the harmonic context at each point in the music [24]. It has been shown that harmonic modelling improves search and retrieval in music databases, for example in order to ﬁnd variations of an example query [36], which is useful for musicological research. Theories of music cognition, if expressed unambiguously, can be implemented and tested on large data corpora and compared with human annotations, in order to verify or reﬁne concepts in the theory. 1

Progress is evident for example in the annual MIREX series of evaluations of music information retrieval systems (http://www.music-ir.org/mirex/wiki/2010: Main_Page)

Probabilistic and Logic-Based Modelling of Harmony

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The remainder of the paper is structured as follows. The next section provides an overview of research in harmony modelling. This is followed by a section describing our probabilistic model of chord transcription. In section 4, we present our logic-based approach to modelling of harmony, and show how this can be used to characterise and classify music. The ﬁnal section is a brief conclusion and outline of future work.

2

Background

Research into computational analysis of harmony has a history of over four decades since [44] proposed a grammar-based analysis that required the user to manually remove any non-harmonic notes (e.g. passing notes, suspensions and ornaments) before the algorithm processed the remaining chord sequence. A grammar-based approach was also taken by [40], who developed a set of chord substitution rules, in the form of a context-free grammar, for generating 12-bar Blues sequences. [31] addressed the problem of extracting patterns and substitution rules automatically from jazz standard chord sequences, and discussed how the notions of expectation and surprise are related to the use of these patterns and rules. Closely related to grammar-based approaches are rule-based approaches, which were used widely in early artiﬁcial intelligence systems. [21] used an elimination process combined with heuristic rules in order to infer the tonality given a fugue melody from Bach’s Well-Tempered Clavier. [15] presents an expert system consisting of about 350 rules for generating 4-part harmonisations of melodies in the style of Bach Chorales. The rules cover the chord sequences, including cadences and modulations, as well as the melodic lines of individual parts, including voice leading. [28] developed an expert system with a complex set of rules for recognising consonances and dissonances in order to infer the chord sequence. Maxwell’s approach was not able to infer harmony from a melodic sequence, as it considered the harmony at any point in time to be deﬁned by a subset of the simultaneously sounding notes. [41] addressed some of the weaknesses of earlier systems with a combined rhythmic and harmonic analysis system based on preference rules [20]. The system assigns a numerical score to each possible interpretation based on the preference rules which the interpretation satisﬁes, and searches the space of all solutions using dynamic programming restricted with a beam search. The system beneﬁts from the implementation of rules relating harmony and metre, such as the preference rule which favours non-harmonic notes occurring on weak metrical positions. One claimed strength of the approach is the transparency of the preference rules, but this is oﬀset by the opacity of the system parameters such as the numeric scores which are assigned to each rule. [33] proposed a counting scheme for matching performed notes to chord templates for variable-length segments of music. The system is intentionally simplistic, in order that the framework might easily be extended or modiﬁed. The main contributions of the work are the graph search algorithms, inspired by Temperley’s dynamic programming approach, which determine the segmentation to be

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used in the analysis. The proposed graph search algorithm is shown to be much more eﬃcient than standard algorithms without diﬀering greatly in the quality of analyses it produces. As an alternative to the rule-based approaches, which suﬀer from the cumulative eﬀects of errors, [38] proposed a probabilistic approach to functional harmonic analysis, using a hidden Markov model. For each time unit (measure or half-measure), their system outputs the current key and the scale degree of the current chord. In order to make the computation tractable, a number of simplifying assumptions were made, such as the symmetry of all musical keys. Although this reduced the number of parameters by at least two orders of magnitude, the training algorithm was only successful on a subset of the parameters, and the remaining parameters were set by hand. An alternative stream of research has been concerned with multidimensional representations of polyphonic music [10,11,42] based on the Viewpoints approach of [12]. This representation scheme is for example able to preserve information about voice leading which is otherwise lost by approaches that treat harmony as a sequence of chord symbols. Although most research has focussed on analysing musical works, some work investigates the properties of entire corpora. [25] compared two corpora of chord sequences, belonging to jazz standards and popular (Beatles) songs respectively, and found key- and context-independent patterns of chords which occurred frequently in each corpus. [26] examined the statistics of the chord sequences of several thousand songs, and compared the results to those from a standard natural language corpus in an attempt to ﬁnd lexical units in harmony that correspond to words in language. [34,35] investigated whether stochastic language models including naive Bayes classiﬁers and 2-, 3- and 4-grams could be used for automatic genre classiﬁcation. The models were tested on both symbolic and audio data, where an oﬀ-the-shelf chord transcription algorithm was used to convert the audio data to a symbolic representation. [39] analysed the Beatles corpus using probabilistic N-grams in order to show that the dependency of a chord on its context extends beyond the immediately preceding chord (the ﬁrst-order Markov assumption). [9] studied diﬀerences in the use of harmony across various periods of classical music history, using root progressions (i.e. the sequence of root notes of chords in a progression) reduced to 2 categories (dominant and subdominant) to give a representation called harmonic vectors. The use of root progressions is one of the representations we use in our own work in section 4 [2]. All of the above systems process symbolic input, such as that found in a score, although most of the systems do not require the level of detail provided by the score (e.g. key signature, pitch spelling), which they are able to reconstruct from the pitch and timing data. In recent years, the focus of research has shifted to the analysis of audio ﬁles, starting with the work of [16], who computed a chroma representation (salience of frequencies representing the 12 Western pitch classes, independent of octave) which was matched to a set of chord templates using the inner product. Alternatively, [7] modelled chords with a 12-dimensional Gaussian distribution, where chord notes had a mean of 1, non-chord notes had a mean of 0,

Probabilistic and Logic-Based Modelling of Harmony

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and the covariance matrix had high values between pairs of chord notes. A hidden Markov model was used to infer the most likely sequence of chords, where state transition probabilities were initialised based on the distance between chords on a special circle of ﬁfths which included minor chords near to their relative major chord. Further work on audio-based harmony analysis is reviewed thoroughly in three recent doctoral theses, to which the interested reader is referred [22,18,32].

3

A Probabilistic Model for Chord Transcription

Music theory, perceptual studies, and musicians themselves generally agree that no musical quality can be treated individually. When a musician transcribes the chords of a piece of music, the chord labels are not assigned solely on the basis of local pitch content of the signal. Musical context such as the key, metrical position and even the large-scale structure of the music play an important role in the interpretation of harmony. [17, Chapter 4] conducted a survey among human music transcription experts, and found that they use several musical context elements to guide the transcription process: not only is a prior rough chord detection the basis for accurate note transcription, but the chord transcription itself depends on the tonal context and other parameters such as beats, instrumentation and structure. The goal of our recent work on chord transcription [24,22,23] is to propose computational models that integrate musical context into the automatic chord estimation process. We employ a dynamic Bayesian network (DBN) to combine models of metrical position, key, chord, bass note and beat-synchronous bass and treble chroma into a single high-level musical context model. The most probable sequence of metrical positions, keys, chords and bass notes is estimated via Viterbi inference. A DBN is a graphical model representing a succession of simple Bayesian networks in time. These are assumed to be Markovian and time-invariant, so the model can be expressed recursively in two time slices: the initial slice and the recursive slice. Our DBN is shown in Figure 1. Each node in the network represents a random variable, which might be an observed node (in our case the bass and treble chroma) or a hidden node (the key, metrical position, chord and bass pitch class nodes). Edges in the graph denote dependencies between variables. In our DBN the musically interesting behaviour is modelled in the recursive slice, which represents the progress of all variables from one beat to the next. In the following paragraphs we explain the function of each node. Chord. Technically, the dependencies of the random variables are described in the conditional probability distribution of the dependent variable. Since the highest number of dependencies join at the chord variable, it takes a central position in the network. Its conditional probability distribution is also the most complex: it depends not only on the key and the metrical position, but also on the chord variable in the previous slice. The chord variable has 121 diﬀerent chord states (see below), and its dependency on the previous chord variable enables

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metric pos.

Mi−1

Mi

key

Ki−1

Ki

chord

Ci−1

Ci

bass

Bi−1

Bi

bass chroma

bs Xi−1

Xibs

treble chroma

tr Xi−1

Xitr

Fig. 1. Our network model topology, represented as a DBN with two slices and six layers. The clear nodes represent random variables, while the observed ones are shaded grey. The directed edges represent the dependency structure. Intra-slice dependency edges are drawn solid, inter-slice dependency edges are dashed.

the reinforcement of smooth sequences of these states. The probability distribution of chords conditional on the previous chord strongly favours the chord that was active in the previous slice, similar to a high self-transition probability in a hidden Markov model. While leading to a chord transcription that is stable over time, dependence on the previous chord alone is not suﬃcient to model adherence to the key. Instead, it is modelled conditionally on the key variable: the probability distribution depends on the chord’s ﬁt with the current key, based on an expert function motivated by Krumhansl’s chord-key ratings [19, page 171]. Finally, the chord variable’s dependency on the metrical position node allows us to favour chord changes at strong metrical positions to achieve a transcription that resembles more closely that of a human transcriber.

Probabilistic and Logic-Based Modelling of Harmony

7

4

2

density

3

1

(a) metrical position model

0

0.2

0.6 0.4 note salience

0.8

1

(b) model of a single chroma pitch class Fig. 2

Key and metrical position. The dependency structure of the key and metrical position variables are comparatively simpler, since they depend only on the respective predecessor. The emphasis on smooth, stable key sequences is handled in the same way as it is in chords, but the 24 states representing major and minor keys have even higher self-transition probability, and hence they will persist for longer stretches of time. The metrical position model represents a 44 meter and hence has four states. The conditional probability distribution strongly favours “normal” beat transitions, i.e. from one beat to the next, but it also allows for irregular transitions in order to accommodate temporary deviations from 44 meter and occasional beat tracking errors. In Figure 2a black arrows represent a transition probability of 1−ε (where ε = 0.05) to the following beat. Grey arrows represent a probability of ε/2 to jump to diﬀerent beats through self-transition or omission of the expected beat. Bass. The random variable that models the bass has 13 states, one for each of the pitch classes, and one “no bass” state. It depends on both the current chord and the previous chord. The current chord is the basis of the most probable bass notes that can be chosen. The highest probability is assigned to the “nominal” chord bass pitch class2 , lower probabilities to the remaining chord pitch classes, and the rest of the probability mass is distributed between the remaining pitch classes. The additional use of the dependency on the previous chord allows us to model the behaviour of the bass note on the ﬁrst beat of the chord diﬀerently from its behaviour on later beats. We can thus model the tendency for the played bass note to coincide with the “nominal” bass note of the chord (e.g. the note B in the B7 chord), while there is more variation in the bass notes played during the rest of the duration of the chord. Chroma. The chroma nodes provide models of the bass and treble chroma audio features. Unlike the discrete nodes previously discussed, they are continuous because the 12 elements of the chroma vector represent relative salience, which 2

The chord symbol itself always implies a bass note, but the bass line might include other notes not speciﬁed by the chord symbol, as in the case of walking bass.

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can assume any value between zero and unity. We represent both bass and treble chroma as multidimensional Gaussian random variables. The bass chroma variable has 13 diﬀerent Gaussians, one for every bass state, and the treble chroma node has 121 Gaussians, one for every chord state. The means of the Gaussians are set to reﬂect the nature of the chords: to unity for pitch classes that are part of the chord, and to zero for the rest. A single variate in the 12-dimensional Gaussian treble chroma distribution models one pitch class, as illustrated in Figure 2b. Since the chroma values are normalised to the unit interval, the Gaussian model functions similar to a regression model: for a given chord the Gaussian density increases with increasing salience of the chord notes (solid line), and decreases with increasing salience of non-chord notes (dashed line). For more details see [22]. One important aspect of the model is the wide variety of chords it uses. It models ten diﬀerent chord types (maj, min, maj/3, maj/5, maj6, 7, maj7, min7, dim, aug) and the “no chord” class N. The chord labels with slashes denote chords whose bass note diﬀers from the chord root, for example D/3 represents a D major chord in ﬁrst inversion (sometimes written D/F). The recognition of these chords is a novel feature of our chord recognition algorithm. Figure 3 shows a score rendered using exclusively the information in our model. In the last four bars, marked with a box, the second chord is correctly annotated as D/F. The position of the bar lines is obtained from the metrical position variable, the key signature from the key variable, and the bass notes from the bass variable. The chord labels are obtained from the chord variable, replicated as notes in the treble staﬀ for better visualisation. The crotchet rest on the ﬁrst beat of the piece indicates that here, the Viterbi algorithm inferred that the “no chord” model ﬁts best. Using a standard test set of 210 songs used in the MIREX chord detection task, our basic model achieved an accuracy of 73%, with each component of the model contributing signiﬁcantly to the result. This improves on the best result at G

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B

Em

7

G

C

F C

G

D/F

Em

7

Bm G

Fig. 3. Excerpt of automatic output of our algorithm (top) and song book version (bottom) of the pop song “Friends Will Be Friends” (Deacon/Mercury). The song book excerpt corresponds to the four bars marked with a box.

Probabilistic and Logic-Based Modelling of Harmony

9

It Won’t Be Long chorus

ground truth segmentation

part n1

automatic segmentation

verse

chorus

bridge

part A

part B

verse

chorus

bridge

part A

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chorus

outro

part A

part n2

chord correct using auto seg. chord correct 1 baseline meth.0.5 0

0

20

40

60

80

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120

time/s

Fig. 4. Segmentation and its eﬀect on chord transcription for the Beatles’ song “It Won’t Be Long” (Lennon/McCartney). The top 2 rows show the human and automatic segmentation respectively. Although the structure is diﬀerent, the main repetitions are correctly identiﬁed. The bottom 2 rows show (in black) where the chord was transcribed correctly by our algorithm using (respectively not using) the segmentation information.

MIREX 2009 for pre-trained systems. Further improvements have been made via two extensions of this model: taking advantage of repeated structural segments (e.g. verses or choruses), and reﬁning the front-end audio processing. Most musical pieces have segments which occur more than once in the piece, and there are two reasons for wishing to identify these repetitions. First, multiple sets of data provide us with extra information which can be shared between the repeated segments to improve detection performance. Second, in the interest of consistency, we can ensure that the repeated sections are labelled with the same set of chord symbols. We developed an algorithm that automatically extracts the repetition structure from a beat-synchronous chroma representation [27], which ranked ﬁrst in the 2009 MIREX Structural Segmentation task. After building a similarity matrix based on the correlation between beatsynchronous chroma vectors, the method ﬁnds sets of repetitions whose elements have the same length in beats. A repetition set composed of n elements with length d receives a score of (n − 1)d, reﬂecting how much space a hypothetical music editor could save by typesetting a repeated segment only once. The repetition set with the maximum score (“part A” in Figure 4) is added to the ﬁnal list of structural elements, and the process is repeated on the remainder of the song until no valid repetition sets are left. The resulting structural segmentation is then used to merge the chroma representations of matching segments. Despite the inevitable errors propagated from incorrect segmentation, we found a signiﬁcant performance increase (to 75% on the MIREX score) by using the segmentation. In Figure 4 the beneﬁcial eﬀect of using the structural segmentation can clearly be observed: many of the white stripes representing chord recognition errors are eliminated by the structural segmentation method, compared to the baseline method.

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S. Dixon, M. Mauch, and A. Anglade

A further improvement was achieved by modifying the front end audio processing. We found that by learning chord proﬁles as Gaussian mixtures, the recognition rate of some chords can be improved. However this did not result in an overall improvement, as the performance on the most common chords decreased. Instead, an approximate pitch transcription method using non-negative least squares was employed to reduce the eﬀect of upper harmonics in the chroma representations [23]. This results in both a qualitative (reduction of speciﬁc errors) and quantitative (a substantial overall increase in accuracy) improvement in results, with a MIREX score of 79% (without using segmentation), which again is signiﬁcantly better than the state of the art. By combining both of the above enhancements we reach an accuracy of 81%, a statistically signiﬁcant improvement over the best result (74%) in the 2009 MIREX Chord Detection tasks and over our own previously mentioned results.

4

Logic-Based Modelling of Harmony

First order logic (FOL) is a natural formalism for representing harmony, as it is suﬃciently general for describing combinations and sequences of notes of arbitrary complexity, and there are well-studied approaches for performing inference, pattern matching and pattern discovery using subsets of FOL. A further advantage of logic-based representations is that a system’s output can be presented in an intuitive way to non-expert users. For example, a decision tree generated by our learning approach provides much more intuition about what was learnt than would a matrix of state transition probabilities. In this work we focus in particular on inductive logic programming (ILP), which is a machine learning approach using logic programming (a subset of FOL) to uniformly represent examples, background knowledge and hypotheses. An ILP system takes as input a set of positive and negative examples of a concept, plus some background knowledge, and outputs a logic program which “explains” the concept, in the sense that all of the positive examples but (ideally) none of the negative examples can be derived from the logic program and background knowledge. ILP has been used for various musical tasks, including inference of harmony [37] and counterpoint [30] rules from musical examples, as well as rules for expressive performance [43]. In our work, we use ILP to learn sequences of chords that might be characteristic of a musical style [2], and test the models on classiﬁcation tasks [3,4,1]. In each case we represent the harmony of a piece of music by a list of chords, and learn models which characterise the various classes of training data in terms of features derived from subsequences of these chord lists. 4.1

Style Characterisation

In our ﬁrst experiments [2], we analysed two chord corpora, consisting of the Beatles studio albums (180 songs, 14132 chords) and a set of jazz standards from the Real Book (244 songs, 24409 chords) to ﬁnd harmonic patterns that diﬀerentiate the two corpora. Chord sequences were represented in terms of the interval between successive root notes or successive bass notes (to make the

Probabilistic and Logic-Based Modelling of Harmony

11

sequences key-independent), plus the category of each chord (reduced to a triad except in the case of the dominant seventh chord). For the Beatles data, where the key had been annotated for each piece, we were also able to express the chord symbols in terms of the scale degree relative to the key, rather than its pitch class, giving a more musically satisfying representation. Chord sequences of length 4 were used, which we had previously found [25] to be a good compromise of suﬃcient length to capture the context (and thus the function) of the chords, without the sequences being overspeciﬁc, in which case few or no patterns would be found. Two models were built, one using the Beatles corpus as positive examples and the other using the Real Book corpus as positive examples. The ILP system Aleph was employed, which ﬁnds a minimal set of rules which cover (i.e. describe) all positive examples (and a minimum number of negative examples). The models built by Aleph consisted of 250 rules for the Beatles corpus and 596 rules for the Real Book. Note that these rules cover every 4-chord sequence in every song, so it is only the rules which cover many examples that are relevant in terms of characterising the corpus. Also, once a sequence has been covered, it is not considered again by the system, so the output is dependent on the order of presentation of the examples. We brieﬂy discuss some examples of rules with the highest coverage. For the Beatles corpus, the highest coverage (35%) was the 4-chord sequence of major triads (regardless of roots). Other highly-ranked patterns of chord categories (5% coverage) had 3 major triads and one minor triad in the sequence. This is not surprising, in that popular music generally has a less rich harmonic vocabulary than jazz. Patterns of root intervals were also found, including a [perfect 4th, perfect 5th, perfect 4th] pattern (4%), which could for example be interpreted as a I - IV - I - IV progression or as V - I - V - I. Since the root interval does not encode the key, it is not possible to distinguish between these interpretations (and it is likely that the data contains instances of both). At 2% coverage, the interval sequence [perfect 4th, major 2nd, perfect 4th] (e.g. I - IV - V - I) is another well-known chord sequence. No single rule covered as many Real Book sequences as the top rule for the Beatles, but some typical jazz patterns were found, such as [perfect 4th, perfect 4th, perfect 4th] (e.g. ii - V - I - IV, coverage 8%), a cycle of descending ﬁfths, and [major 6th, perfect 4th, perfect 4th] (e.g. I - vi - ii - V, coverage 3%), a typical turnaround pattern. One weakness with this ﬁrst experiment, in terms of its goal as a pattern discovery method, is that the concept to learn and the vocabulary to describe it (deﬁned in the background knowledge) need to be given in advance. Diﬀerent vocabularies result in diﬀerent concept descriptions, and a typical process of concept characterisation is interactive, involving several reﬁnements of the vocabulary in order to obtain an interesting theory. Thus, as we reﬁne the vocabulary we inevitably reduce the problem to a pattern matching task rather than pattern discovery. A second issue is that since musical styles have no formal deﬁnition, it is not possible to quantify the success of style characterisation

12

S. Dixon, M. Mauch, and A. Anglade

directly, but only indirectly, by using the learnt models to classify unseen examples. Thus the following harmony modelling experiments are evaluated via the task of genre classiﬁcation. 4.2

Genre Classification

For the subsequent experiments we extended the representation to allow variable length patterns and used TILDE, a ﬁrst-order logic decision tree induction algorithm for modelling harmony [3,4]. As test data we used a collection of 856 pieces (120510 chords) covering 3 genres, each of which was divided into a further 3 subgenres: academic music (Baroque, Classical, Romantic), popular music (Pop, Blues, Celtic) and jazz (Pre-bop, Bop, Bossa Nova). The data is represented in the Band in a Box format, containing a symbolic encoding of the chords, which were extracted and encoded using a deﬁnite clause grammar (DCG) formalism. The software Band in a Box is designed to produce an accompaniment based on the chord symbols, using a MIDI synthesiser. In further experiments we tested the classiﬁcation method using automatic chord transcription (see section 3) from the synthesised audio data, in order to test the robustness of the system to errors in the chord symbols. The DCG representation was developed for natural language processing to express syntax or grammar rules in a format which is both human-readable and machine-executable. Each predicate has two arguments (possibly among other arguments), an input list and an output list, where the output list is always a suﬃx of the input list. The diﬀerence between the two lists corresponds to the subsequence described by the predicate. For example, the predicate gap(In,Out) states that the input list of chords (In) commences with a subsequence corresponding to a “gap”, and the remainder of the input list is equal to the output list (Out). In our representation, a gap is an arbitrary sequence of chords, which allows the representation to skip any number of chords at the beginning of the input list without matching them to any harmony concept. Extra arguments can encode parameters and/or context, so that the term degreeAndCategory(Deg,Cat,In,Out,Key) states that the list In begins with a chord of scale degree Deg and chord category Cat in the context of the key Key. Thus the sequence: gap(S,T), degreeAndCategory(2,min7,T,U,gMajor), degreeAndCategory(5,7,U,V,gMajor), degreeAndCategory(1,maj7,V,[],gMajor) states that the list S starts with any chord subsequence (gap), followed by a minor 7th chord on the 2nd degree of G major (i.e. Amin7), followed by a (dominant) 7th chord on the 5th degree (D7) and ending with a major 7th chord on the tonic (Gmaj7). TILDE learns a classiﬁcation model based on a vocabulary of predicates supplied by the user. In our case, we described the chords in terms of their root note,

Probabilistic and Logic-Based Modelling of Harmony

13

genre(g,A,B,Key) gap(A,C),degAndCat(5,maj,C,D,Key),degAndCat(1,min,D,E,Key) ? Y

N

academic

gap(A,F),degAndCat(2,7,F,G,Key),degAndCat(5,maj,G,H,Key) ? N

Y ... gap(H,I),degAndCat(1,maj,I,J,Key),degAndCat(5,7,J,K,Key) ? Y academic

N Y

jazz

gap(H,L),degAndCat(2,min7,L,M,Key),degAndCat(5,7,M,N,Key) ? jjazz N academic

Fig. 5. Part of the decision tree for a binary classiﬁer for the classes Jazz and Academic Table 1. Results compared with the baseline for 2-class, 3-class and 9-class classiﬁcation tasks Classiﬁcation Task Baseline Symbolic Audio Academic – Jazz 0.55 0.947 0.912 Academic – Popular 0.55 0.826 0.728 Jazz – Popular 0.61 0.891 0.807 Academic – Popular – Jazz 0.40 0.805 0.696 All 9 subgenres 0.21 0.525 0.415

scale degree, chord category, and intervals between successive root notes, and we constrained the learning algorithm to generate rules containing subsequences of length at least two chords. The model can be expressed as a decision tree, as shown in ﬁgure 5, where the choice of branch taken is based on whether or not the chord sequence matches the predicates at the current node, and the class to which the sequence belongs is given by the leaf of the decision tree reached by following these choices. The decision tree is equivalent to an ordered set of rules or a Prolog program. Note that a rule at a single node of a tree cannot necessarily be understood outside of its context in the tree. In particular, a rule by itself cannot be used as a classiﬁer. The results for various classiﬁcation tasks are shown in Table 1. All results are signiﬁcantly above the baseline, but performance clearly decreases for more difﬁcult tasks. Perfect classiﬁcation is not to be expected from harmony data, since other aspects of music such as instrumentation (timbre), rhythm and melody are also involved in deﬁning and recognising musical styles. Analysis of the most common rules extracted from the decision tree models built during these experiments reveals some interesting and well-known jazz, academic and popular music harmony patterns. For each rule shown below, the coverage expresses the fraction of songs in each class that match the rule. For example, while a perfect cadence is common to both academic and jazz styles, the chord categories distinguish the styles very well, with academic music using triads and jazz using seventh chords:

14

S. Dixon, M. Mauch, and A. Anglade

genre(academic,A,B,Key) :- gap(A,C), degreeAndCategory(5,maj,C,D,Key), degreeAndCategory(1,maj,D,E,Key), gap(E,B). [Coverage: academic=133/235; jazz=10/338] genre(jazz,A,B,Key) :- gap(A,C), degreeAndCategory(5,7,C,D,Key), degreeAndCategory(1,maj7,D,E,Key), gap(E,B). [Coverage: jazz=146/338; academic=0/235] A good indicator of blues is the sequence: ... - I7 - IV7 - ... genre(blues,A,B,Key) :- gap(A,C), degreeAndCategory(1,7,C,D,Key), degreeAndCategory(4,7,D,E,Key), gap(E,B). [Coverage: blues=42/84; celtic=0/99; pop=2/100] On the other hand, jazz is characterised (but not exclusively) by the sequence: ... - ii7 - V7 - ... genre(jazz,A,B,Key) :- gap(A,C), degreeAndCategory(2,min7,C,D,Key), degreeAndCategory(5,7,D,E,Key), gap(E,B). [Coverage: jazz=273/338; academic=42/235; popular=52/283] The representation also allows for longer rules to be expressed, such as the following rule describing a modulation to the dominant key and back again in academic music: ... - II7 - V - ... - I - V7 - ... genre(academic,A,B,Key) :- gap(A,C), degreeAndCategory(2,7,C,D,Key), degreeAndCategory(5,maj,D,E,Key), gap(E,F), degreeAndCategory(1,maj,F,G,Key), degreeAndCategory(5,7,G,H,Key), gap(H,B). [Coverage: academic=75/235; jazz=0/338; popular=1/283]

Probabilistic and Logic-Based Modelling of Harmony

15

Although none of the rules are particularly surprising, these examples illustrate some meaningful musicological concepts that are captured by the rules. In general, we observed that Academic music is characterised by rules establishing the tonality, e.g. via cadences, while Jazz is less about tonality, and more about harmonic colour, e.g. the use of 7th, 6th, augmented and more complex chords, and Popular music harmony tends to have simpler harmonic rules as melody is predominant in this style. The system is also able to ﬁnd longer rules that a human might not spot easily. Working from audio data, even though the transcriptions are not fully accurate, the classiﬁcation and rules still capture the same general trends as for symbolic data. For genre classiﬁcation we are not advocating a harmony-based approach alone. It is clear that other musical features are better predictors of genre. Nevertheless, the positive results encouraged a further experiment in which we integrated the current classiﬁcation approach with a state-of-the-art genre classiﬁcation system, to test whether the addition of a harmony feature could improve its performance. 4.3

Genre Classification Using Harmony and Low-Level Features

In recent work [1] we developed a genre classiﬁcation framework combining both low-level signal-based features and high-level harmony features. A state-of-theart statistical genre classiﬁer [8] using 206 features, covering spectral, temporal, energy, and pitch characteristics of the audio signal, was extended using a random forest classiﬁer containing rules for each genre (classical, jazz and pop) derived from chord sequences. We extended our previous work using the ﬁrstorder logic induction algorithm TILDE, to learn a random forest instead of a single decision tree from the chord sequence corpus described in the previous genre classiﬁcation experiments. The random forest model achieved better classiﬁcation rates (88% on the symbolic data and 76% on the audio data) for the three-class classiﬁcation problem (previous results 81% and 70% respectively). Having trained the harmony classiﬁer, its output was added as an extra feature to the low-level classiﬁer and the combined classiﬁer was tested on three-genre subsets of two standard genre classiﬁcation data sets (GTZAN and ISMIR04) containing 300 and 448 recordings respectively. Multilayer perceptrons and support vector machines were employed to classify the test data using 5×5-fold cross-validation and feature selection. Results are shown in table 2 for the support vector machine classiﬁer, which outperformed the multilayer perceptrons. Results indicate that the combination of low-level features with the harmonybased classiﬁer produces improved genre classiﬁcation results despite the fact Table 2. Best mean classiﬁcation results (and number of features used) for the two data sets using 5×5-fold cross-validation and feature selection Classiﬁer GTZAN data set ISMIR04 data set SVM without harmony feature 0.887 (60 features) 0.938 (70 features) SVM with harmony feature 0.911 (50 features) 0.953 (80 features)

16

S. Dixon, M. Mauch, and A. Anglade

that the classiﬁcation rate of the harmony-based classiﬁer alone is poor. For both datasets the improvements over the standard classiﬁer (as shown in table 2) were found to be statistically signiﬁcant.

5

Conclusion

We have looked at two approaches to the modelling of harmony which aim to “dig deeper into the music”. In our probabilistic approach to chord transcription, we demonstrated the advantage of modelling musical context such as key, metrical structure and bass line, and simultaneously estimating all of these variables along with the chord. We also developed an audio feature using non-negative least squares that reﬂects the notes played better than the standard chroma feature, and therefore reduces interference from harmonically irrelevant partials and noise. A further improvement of the system was obtained by modelling the global structure of the music, identifying repeated sections and averaging features over these segments. One promising avenue of further work is the separation of the audio (low-level) and symbolic (high-level) models which are conceptually distinct but modelled together in current systems. A low-level model would be concerned only with the production or analysis of audio — the mapping from notes to features; while a high-level model would be a musical model handling the mapping from chord symbols to notes. Using a logic-based approach, we showed that it is possible to automatically discover patterns in chord sequences which characterise a corpus of data, and to use such models as classiﬁers. The advantage with a logic-based approach is that models learnt by the system are transparent: the decision tree models can be presented to users as sets of human readable rules. This explanatory power is particularly relevant for applications such as music recommendation. The DCG representation allows chord sequences of any length to coexist in the same model, as well as context information such as key. Our experiments found that the more musically meaningful Degree-and-Category representation gave better classiﬁcation results than using root intervals. The results using transcription from audio data were encouraging in that although some information was lost in the transcription process, the classiﬁcation results remained well above the baseline, and thus this approach is still viable when symbolic representations of the music are not available. Finally, we showed that the combination of high-level harmony features with low-level features can lead to genre classiﬁcation accuracy improvements in a state-of-the-art system, and believe that such high-level models provide a promising direction for genre classiﬁcation research. While these methods have advanced the state of the art in music informatics, it is clear that in several respects they are not yet close to an expert musician’s understanding of harmony. Limiting the representation of harmony to a list of chord symbols is inadequate for many applications. Such a representation may be suﬃcient as a memory aid for jazz and pop musicians, but it allows only a very limited speciﬁcation of chord voicing (via the bass note), and does not permit analysis of polyphonic texture such as voice leading, an important concept in many harmonic styles, unlike the recent work of [11] and [29]. Finally, we note

Probabilistic and Logic-Based Modelling of Harmony

17

that the current work provides little insight into harmonic function, for example the ability to distinguish harmony notes from ornamental and passing notes and to recognise chord substitutions, both of which are essential characteristics of a system that models a musician’s understanding of harmony. We hope to address these issues in future work. Acknowledgements. This work was performed under the OMRAS2 project, supported by the Engineering and Physical Sciences Research Council, grant EP/E017614/1. We would like to thank Chris Harte, Matthew Davies and others at C4DM who contributed to the annotation of the audio data, and the Pattern Recognition and Artiﬁcial Intelligence Group at the University of Alicante, who provided the Band in a Box data.

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Interactive Music Applications and Standards Rebecca Stewart, Panos Kudumakis, and Mark Sandler Queen Mary, University of London, London, UK {rebecca.stewart,panos.kudumakis,mark.sandler}@eecs.qmul.ac.uk http://www.elec.qmul.ac.uk/digitalmusic

Abstract. Music is now consumed in interactive applications that allow for the user to directly influence the musical performance. These applications are distributed as games for gaming consoles and applications for mobile devices that currently use proprietary file formats, but standardization orgranizations have been working to develop an interchangeable format. This paper surveys the applications and their requirements. It then reviews the current standards that address these requirements focusing on the MPEG Interactive Music Application Format. The paper closes by looking at additional standards that address similar applications and outlining the further requirements that need to be met. Keywords: interactive music, standards.

1

Introduction

The advent of the Internet and the exploding popularity of ﬁle sharing web sites have challenged the music industry’s traditional supply model that relied on the physical distribution of music recordings such as vinyl records, cassettes, CDs, etc [5], [3]. In this direction, new interactive music services have emerged [1], [6], [7]. However, a standardized ﬁle format is inevitably required to provide the interoperability between various interactive music players and interactive music applications. Video games and music consumption, once discrete markets, are now merging. Games for dedicated gaming consoles such as the Microsoft XBox, Nintendo Wii and Sony Playstation and applications for smart phones using the Apple iPhone and Google Android platforms are incorporating music creation and manipulation into applications which encourage users to purchase music. These games can even be centered around speciﬁc performers such as the Beatles [11] or T-Pain [14]. Many of these games follow a format inspired by karaoke. In its simplest case, audio processing for karaoke applications involves removing the lead vocals so that a live singer can perform with the backing tracks. This arrangement grew in complexity by including automatic lyric following as well. Karaoke performance used to be relegated to a setup involving a sound system with microphone and playback capabilities within a dedicated space such as a karaoke bar or living room, but it has found a revitalized market with mobile devices such as smart S. Ystad et al. (Eds.): CMMR 2010, LNCS 6684, pp. 20–30, 2011. c Springer-Verlag Berlin Heidelberg 2011

Interactive Music Applications and Standards

21

phones. Karaoke is now no longer limited to a certain place or equipment, but can performed with a group of friends with a gaming console in a home or performed with a smart phone, recorded and uploaded online to share with others. A standard format is needed to allow for the same musical content to be produced once and used with multiple applications. We will look at the current commercial applications for interactive music and discuss what requirements need to be met. We will then look at three standards that address these requirements: the MPEG-A Interactive Music Application Format (IM AF), IEEE 1599 and interaction eXtensible Music Format (iXMF). We conclude by discussing what improvements still need to be made for these standards to meet the requirements of currently commercially-available applications.

2

Applications

Karaoke-inﬂuenced video games have become popular as titles such as Guitar Hero and Rock Band have brought interactive music to a broad market [11]. The video games are centered around games controllers that emulate musical instruments such as the guitar and drum set. The players follow the music as they would in karaoke, but instead of following lyrics and singing, they follow colored symbols which indicate when to press the corresponding button. With Rock Band karaoke singing is available – backing tracks and lyrics are provided so that a player can sing along. However, real-time pitch-tracking has enhanced the gameplay as the player’s intonation and timing are scored. The company Smule produces applications for the Apple iPhone, iPod Touch and iPad. One of their most popular applications for the platform is called I Am T-Pain [14]. The application allows users to sing into their device and automatically processes their voice with the auto-tune eﬀects that characterize the artist T-Pain’s vocals. The user can do this in a karaoke-style performance by purchasing and downloading ﬁles containing the backing music to a selection of T-Pain’s released tracks. The song’s lyrics then appear on the screen synchronized with the music as for karaoke, and the user’s voice is automatically processed with an auto-tune eﬀect. The user can change the auto-tune settings to change the key and mode or use a preset. The freestyle mode allows the user to record their voice without music and with the auto-tuner. All of the user’s performances can be recorded and uploaded online and easily shared on social networks. Smule has built on the karaoke concept with the release of Glee Karaoke [13]. The application is branded by the US TV show Glee and features the music performed on the show. Like the I Am T-Pain application, Glee Karaoke lets users purchase and download music bundled with lyrics so that they can perform the vocal portion of the song themselves. Real-time pitch correction and automatic three-part harmony generation are available to enhance the performance. Users can also upload performances to share online, but unlike I Am T-Pain, Glee Karaoke users can participate in a competitive game. Similar to the Guitar Hero and Rock Band games, users get points for completing songs and for correctly singing on-pitch.

22

3

R. Stewart, P. Kudumakis, and M. Sandler

Requirements

If the music industry continues to produce content for interactive music applications, a standard distribution format is needed. Content then will not need to be individually authored for each application. At the most basic level, a standard needs to allow: – Separate tracks or groups of tracks – Apply signal processing to those tracks or groups – Markup those tracks or stems to include time-based symbolic information Once tracks or groups of tracks are separated from the full mix of the song, additional processing or information can be included to enhance the interactivity with those tracks. 3.1

Symbolic Information

Karaoke-style applications involving singing require lyrical information as a bare minimum, though it is expected that that information is time-aligned with the audio content. As seen in Rock Band, I Am T-Pain and Glee Karaoke, additional information regarding the correct pitch and timing is also needed. A standard for interactive music applications also needs to accommodate multiple parallel sequences of notes. This is especially important for multiple player games like Rock Band where each player has a diﬀerent instrument and stream of timings and pitches. 3.2

Audio Signal Processing

The most simplistic interactive model of multiple tracks requires basic mixing capabilities so that those tracks can be combined to create a single mix of the song. A traditional karaoke mix could easily be created within this model by muting the vocal track, but this model could also be extended. Including audio eﬀects as in I Am T-Pain and Glee Karaoke allows users to add musical content (such as their singing voice) to the mix and better emulate the original performance. Spatial audio signal processing is also required for more advanced applications. This could be as simple as panning a track between the left and right channels of a stereo song, but could grow in complexity when considering applications for gaming consoles. Many games allow for surround sound playback, usually over a 5.1 loudspeaker setup, so the optimal standard would allow for ﬂexible loudspeaker conﬁgurations. Mobile applications could take advantage of headphone playback and use binaural audio to create an immersive 3D space.

4

MPEG-A IM AF

The MPEG-A Interactive Music Application Format (IM AF) standard structures the playback of songs that have multiple, unmixed audio tracks [8], [9], [10].

Interactive Music Applications and Standards

23

IM AF creates a container for the tracks, the associated metadata and symbolic data while also managing how the audio tracks are played. Creating an IM AF ﬁle involves formatting diﬀerent types of media data, especially multiple audio tracks with interactivity data and storing them into an ISO-Base Media File Format. An IM AF ﬁle is composed of: Multiple audio tracks representing the music (e.g. instruments and/or voices). Groups of audio tracks – a hierarchical structure of audio tracks (e.g. all guitars of a song can be gathered in the same group). Preset data – pre-deﬁned mixing information on multiple audio tracks (e.g. karaoke and rhythmic version). User mixing data and interactivity rules, information related to user interaction (e.g. track/group selection, volume control). Metadata used to describe a song, music album, artist, etc. Additional media data that can be used to enrich the users interaction space (e.g. timed text synchronized with audio tracks which can represent the lyrics of a song, images related to the song, music album, artist, etc). 4.1

Mixes

The multiple audio tracks are combined to produce a mix. The mix is deﬁned by the playback level of tracks and may be determined by the music content creator or by the end-user. An interactive music player utilizing IM AF could allow users to re-mix music tracks by enabling them to select the number of instruments to be listened to and adjust the volume of individual tracks to their particular taste. Thus, IM AF enables users to publish and exchange this re-mixing data, enabling other users with IM AF players to experience their particular music taste creations. Preset mixes of tracks could also be available. In particular IM AF supports two possible mix modes for interaction and playback: preset-mix mode and user-mix mode. In the preset-mix mode, the user selects one preset among the presets stored in IM AF, and then the audio tracks are mixed using the preset parameters associated with the selected preset. Some preset examples are: General preset – composed of multiple audio tracks by music producer. Karaoke preset – composed of multiple audio tracks except vocal tracks. A cappella preset – composed of vocal and chorus tracks. Figure 1 shows an MPEG-A IM AF player. In user-mix mode, the user selects/deselects the audio tracks/groups and controls the volume of each of them. Thus, in user-mix mode, audio tracks are mixed according to the user’s control and taste; however, they should comply with the interactivity rules stored in the IM AF. User interaction should conform to certain rules deﬁned by the music composers with the aim to ﬁt their artistic creation. However, the rules deﬁnition is optional and up to the music composer, they are not imposed by the IM AF format. In general there are two categories of rules in IM AF: selection and

24

R. Stewart, P. Kudumakis, and M. Sandler

Fig. 1. An interactive music application. The player on the left shows the song being played in a preset mix mode and the player on the right shows the user mix mode.

!

! "

Fig. 2. Logic for interactivity rules and mixes within IM AF

mixing rules. The selection rules relate to the selection of the audio tracks and groups at rendering time whereas the mixing rules relate to the audio mixing. Note that the interactivity rules allow the music producer to deﬁne the amount of freedom available in IM AF users mixes. The interactivity rules analyser in the player veriﬁes whether the user interaction conforms to music producers rules. Figure 2 depicts in a block diagram the logic for both the preset-mix and the user-mix usage modes. IM AF supports four types of selection rules, as follows: Min/max rule specifying both minimum and maximum number of track/ groups of the group that might be in active state. Exclusion rule specifying that several track/groups of a song will never be in the active state at the same time.

Interactive Music Applications and Standards

25

Not mute rule deﬁning a track/group always in the active state. Implication rule specifying that the activation of a track/group implies the activation of another track/group. IM AF also supports four types of mixing rules, as follows: Limits rule specifying the minimum and maximum limits of the relative volume of each track/group. Equivalence rule specifying an equivalence volume relationship between tracks/groups. Upper rule specifying a superiority volume relationship between tracks/groups. Lower rule specifying an inferiority volume relationship between tracks/groups. Backwards compatibility with legacy non-interactive players is also supported by IM AF. For legacy music players or devices that are not capable of simultaneous decoding the multiple audio tracks, a special audio track stored in IM AF ﬁle can still be played. 4.2

File Structure

The ﬁle formats accepted within an IM AF ﬁle are described in Table 1. IM AF holds ﬁles describing images associated with the audio such as an album cover, timed text for lyrics, other metadata allowed in MPEG-7 and the audio content. IM AF also supports a number of brands according to application domain. These depend on the device processing power capabilities (e.g. mobile phone, laptop computer and high ﬁdelity devices) which consequently deﬁne the maximum number of audio tracks that can be decoded simultaneously in an IM AF player running on a particular device. IM AF brands are summarized in Table 2. In all IM AF brands, the associated data and metadata are supported. The IM AF ﬁle format structure is derived from the ISO-Base Media File Format standard. As such it facilitates interchange, management, editing and presentation of diﬀerent type media data and their associated metadata in a ﬂexible and extensible way. The object-oriented nature of ISO-Base Media File Table 1. The file formats accepted within an IM AF file Type

Component Name

File Format ISO Base Media File Format (ISO-BMFF)

Specification ISO/IEC 14496-12:2008

Audio

MPEG-4 Audio AAC Profile MPEG-D SAOC MPEG-1 Audio Layer III (MP3) PCM

ISO/IEC 14496-3:2005 ISO/IEC 23003-2:2010 ISO/IEC 11172-3:1993 -

Image

JPEG

ISO/IEC 10918-1:1994

3GPP Timed Text

3GPP TS 26.245:2004

Text

Metadata MPEG-7 MDS

ISO/IEC 15938-5:2003

26

R. Stewart, P. Kudumakis, and M. Sandler

Table 2. Brands supported by IM AF. For im04 and im12, simultaneously decoded audio tracks consist of tracks related to SAOC, which are a downmix signal and SAOC bitstream. The downmix signal should be encoded using AAC or MP3. For all brands, the maximum channel number of each track is restricted to 2 (stereo). Audio Brands

AAC MP3 SAOC PCM

Max No Max Freq. Tracks /bits

im01

X

X

4

im02

X

X

6

im03

X

X

im04

X

X

im11

X

X

im12

X

im21

X

X

8 X

AAC/Level 2

X

AAC/Level 2

16

32

Mobile

SAOC Baseline/2 AAC/Level 2

Normal

AAC/Level 2

2 X

Application

48 kHz/16 bits

2 X

Profile/ Level

SAOC Baseline/3

96 kHz/24 bits

AAC/Level 5

High-end

Format, inherited in IM AF, enables simplicity in the ﬁle structure in terms of objects that have their own names, sizes and deﬁned speciﬁcations according to their purpose. Figure 3 illustrates the IM AF ﬁle format structure. It mainly consists of ftyp, moov and mdat type information objects/boxes. The ftyp box contains information on ﬁle type and compatibility. The moov box describes the presentation of the scene and usually includes more than one trak boxes. A trak box contains the presentation description for a speciﬁc media type. A media type in each trak box could be audio, image or text. The trak box supports time information for synchronization with media described in other trak boxes. The mdat box contains the media data described in the trak boxes. Instead of a native system ﬁle path, a trak box may include an URL to locate the media data. In this way the mdat box maintains a compact representation enabling consequently eﬃcient exchange and sharing of IM AF ﬁles. Furthermore, in the moov box some speciﬁc information is also included: the group container box grco; the preset container box prco; and the rules container box ruco for storing group, preset and rules information, respectively. The grco box contains zero or more group boxes designated as grup describing the group hierarchy structure of audio tracks and/or groups. The prco box contains one or more prst boxes which describe the predeﬁned mixing information in the absence of user interaction. The ruco box contains zero or more selection rules boxes rusc and/or mixing rules boxes rumx describing the interactivity rules related to selection and/or mixing of audio tracks.

Interactive Music Applications and Standards

27

+

'(%)*

'(%)* ' ' '

!"#$%&

!"#$%&

Fig. 3. IM AF file format

5

Related Formats

While the IM AF packages together the relevant metadata and content that an interactive music application would require, other ﬁle formats have also been developed as a means to organize and describe synchronized streams of information for diﬀerent applications. The two that will be brieﬂy reviewed here are IEEE 1599 [12] and iXMF [4]. 5.1

IEEE 1599

IEEE 1599 is an XML-based format for synchronizing multiple streams of symbolic and non-symbolic data validated against a document type deﬁnition (DTD). It was proposed to IEEE Standards in 2001 and was previously referred to as MX (Musical Application Using XML). The standard emphasizes the readability

28

R. Stewart, P. Kudumakis, and M. Sandler

*

#

$

%

'

&

!

"

" "

! "

' "( ")* "

Fig. 4. The layers in IEEE 1599

of symbols by both humans and machines, hence the decision to represent all information that is not audio or video sample data within XML. The standard is developed primarily for applications that provide additional information surrounding a piece of music. Example applications include being able to easily navigate between a score, multiple recordings of performances of that score and images of the performers in the recordings [2]. The format consists of six layers that communicate with each other, but there can be multiple instances of the same layer type. Figure 4 illustrates how the layers interact. The layers are referred to as: General – holds metadata relevant to entire document. Logic – logical description of score symbols. Structural – description of musical objects and their relationships. Notational – graphical representation of the score. Performance – computer-based descriptions of a musical performance. Audio – digital audio recording. 5.2

iXMF

Another ﬁle format that perform a similar task with a particular focus on video games is iXMF (interaction eXtensible Music Format) [4]. The iXMF standard is targeted for interactive audio within games development. XMF is a meta ﬁle format that bundles multiple ﬁles together and iXMF uses this same meta ﬁle format as its structure.

Interactive Music Applications and Standards

29

iXMF uses a structure in which a moment in time can trigger an event. The triggered event can encompass a wide array of activities such as the playing of an audio ﬁle or the execution of speciﬁc code. The overall structure is described in [4] as: – – – –

An iXMF ﬁle is a collection of Cues. A Cue is a collection of Media Chunks and Scripts. A Media Chunk is a contiguous region in a playable media ﬁle. A Script is rules describing how a Media Chunk is played.

The format allows for both audio and symbolic information information such as MIDI to be included. The Scripts then allow for real-time adaptive audio eﬀects. iXMF has been developed to create interactive soundtracks for video games environments, so the audio can be generated in real-time based on a user’s actions and other external factors. There are a number of standard Scripts that perform basic tasks such as starting or stopping a Cue, but this set of Scripts can also be extended.

6

Discussion

Current commercial applications built around interactive music require real-time playback and interaction with multiple audio tracks. Additionally, symbolic information, including text, is needed to accommodate the new karaoke-like games such as Guitar Hero. The IM AF standard fulﬁls most of the requirements, but not all. In particular it lacks the ability to include symbolic information like MIDI note and instrument data. IEEE 1599 and iXMF both can accommodate MIDI data, though lack some of the advantages of IM AF such as direct integration with a number of MPEG formats. One of the strengths of iXMF is its Scripts which can deﬁne time-varying audio eﬀects. These kind of eﬀects are needed for applications such as I Am T-Pain and Glee Karaoke. IM AF is beginning to consider integrating these eﬀects such as equalization, but greater ﬂexibility will be needed so that the content creators can create and manipulate their own audio signal processing algorithms. The consumer will also need to be able to manually adjust the audio eﬀects applied to the audio in order to build applications like the MXP4 Studio [7] with IM AF. As interactive music applications may be used in a variety of settings, from dedicated gaming consoles to smart phones, any spatialization of the audio needs to be ﬂexible and automatically adjust to the most appropriate format. This could range from stereo speakers to surround sound systems or binaural audio over headphones. IM AF is beginning to support SAOC (Spatial Audio Object Coding) which addresses this very problem and diﬀerentiates it from similar standards. While there are a number of standard ﬁle formats that have been developed in parallel to address slightly diﬀering application areas within interactive music, IM AF is increasingly the best choice for karaoke-style games. There are still

30

R. Stewart, P. Kudumakis, and M. Sandler

underdeveloped or missing features, but by determining the best practice put forth in similar standards, IM AF can become an interchangeable ﬁle format for creators to distribute their music to multiple applications. The question then remains: will the music industry embrace IM AF – enabling interoperability of interactive music services and applications for the beneﬁt of end users – or will it try to lock them down in proprietary standards for the beneﬁt of few oligopolies? Acknowledgments. This work was supported by UK EPSRC Grants: Platform Grant (EP/E045235/1) and Follow On Fund (EP/H008160/1).

References 1. Audizen, http://www.audizen.com (last viewed, February 2011) 2. Ludovico, L.A.: The new standard IEEE 1599, introduction and examples. J. Multimedia 4(1), 3–8 (2009) 3. Goel, S., Miesing, P., Chandra, U.: The Impact of Illegal Peer-to-Peer File Sharing on the Media Industry. California Management Review 52(3) (Spring 2010) 4. IASIG Interactive XMF Workgroup: Interactive XMF specification: file format specification. Draft 0.9.1a (2008), http://www.iasig.org/pubs/ixmf_ draft-v091a.pdf 5. IFPI Digital Musi Report 2009: New Business Models for a Changing Environment. International Federation of the Phonographic Industry (January 2009) 6. iKlax Media, http://www.iklaxmusic.com (last viewed February 2011) 7. Interactive Music Studio by MXP4, Inc., http://www.mxp4.com/ interactive-music-studio (last viewed February 2011) 8. ISO/IEC 23000-12, Information technology – Multimedia application format (MPEG-A) – Part 12: Interactive music application format (2010), http://www.iso.org/iso/iso_catalogue/catalogue_tc/catalogue_detail. htm?csnumber=53644 9. ISO/IEC 23000-12/FDAM 1 IM AF Conformance and Reference Software, N11746, 95th MPEG Meeting, Daegu, S. Korea (2011) 10. Kudumakis, P., Jang, I., Sandler, M.: A new interactive MPEG format for the music industry. In: 7th Int. Symposium on Computer Music Modeling and Retrieval (CMMR 2010), M´ alaga, Spain (2010) 11. Kushner, D.: The Making of the Beatles: Rock Band. IEEE Spectrum 46(9), 30–35 (2009) 12. Ludovico, L.A.: IEEE 1599: a multi-layer approach to music description. J. Multimedia 4(1), 9–14 (2009) 13. Smule, Inc.: Glee Karaoke iPhone Application, http://glee.smule.com/ (last viewed February 2011) 14. Smule, Inc.: I Am T-Pain iPhone Application, http://iamtpain.smule.com/ (last viewed February 2011)

Interactive Music with Active Audio CDs Sylvain Marchand, Boris Mansencal, and Laurent Girin LaBRI – CNRS, University of Bordeaux, France {sylvain.marchand,boris.mansencal}@labri.fr GIPSA-lab – CNRS, Grenoble Institute of Technology, France [email protected]

Abstract. With a standard compact disc (CD) audio player, the only possibility for the user is to listen to the recorded track, passively: the interaction is limited to changing the global volume or the track. Imagine now that the listener can turn into a musician, playing with the sound sources present in the stereo mix, changing their respective volumes and locations in space. For example, a given instrument or voice can be either muted, ampliﬁed, or more generally moved in the acoustic space. This will be a kind of generalized karaoke, useful for disc jockeys and also for music pedagogy (when practicing an instrument). Our system shows that this dream has come true, with active CDs fully backward compatible while enabling interactive music. The magic is that “the music is in the sound”: the structure of the mix is embedded in the sound signal itself, using audio watermarking techniques, and the embedded information is exploited by the player to perform the separation of the sources (patent pending) used in turn by a spatializer. Keywords: interactive music, compact disc, audio watermarking, source separation, sound spatialization.

1

Introduction

Composers of acousmatic music conduct diﬀerent stages through the composition process, from sound recording (generally stereophonic) to diﬀusion (multiphonic). During live interpretation, they interfere decisively on spatialization and coloration of pre-recorded sonorities. For this purpose, the musicians generally use a(n un)mixing console. With two hands, this requires some skill and becomes hardly tractable with many sources or speakers. Nowadays, the public is also eager to interact with the musical sound. Indeed, more and more commercial CDs come with several versions of the same musical piece. Some are instrumental versions (for karaoke), other are remixes. The karaoke phenomenon gets generalized from voice to instruments, in musical video games such as Rock Band 1 . But in this case, to get the interaction the user has to buy the video game, which includes the multitrack recording. Yet, the music industry is still reluctant to release the multitrack version of musical hits. The only thing the user can get is a standard CD, thus a stereo 1

See URL: http://www.rockband.com

S. Ystad et al. (Eds.): CMMR 2010, LNCS 6684, pp. 31–50, 2011. c Springer-Verlag Berlin Heidelberg 2011

32

S. Marchand, B. Mansencal, and L. Girin

mix, or its dematerialized version available for download. The CD is not dead: imagine a CD fully backward compatible while permitting musical interaction. . . We present the proof of concept of the active audio CD, as a player that can read any active disc – in fact any 16-bit PCM stereo sound ﬁle, decode the musical structure present in the sound signal, and use it to perform high-quality source separation. Then, the listener can see and manipulate the sound sources in the acoustic space. Our system is composed of two parts. First, a CD reader extracts the audio data of the stereo track and decodes the musical structure embedded in the audio signal (Section 2). This additional information consists of the combination of active sources for each time-frequency atom. As shown in [16], this permits an informed source separation of high quality (patent pending). In our current system, we get up to 5 individual tracks out of the stereo mix. Second, a sound spatializer is able to map in real time all the sound sources to any position in the acoustic space (Section 3). Our system supports either binaural (headphones) or multi-loudspeaker conﬁgurations. As shown in [14], the spatialization is done in the spectral domain, is based on acoustics and interaural cues, and the listener can control the distance and the azimuth of each source. Finally, the corresponding software implementation is described in Section 4.

2

Source Separation

In this section, we present a general overview of the informed source separation technique which is at the heart of the active CD player. This technique is based on a two-step coder-decoder conﬁguration [16][17], as illustrated on Fig. 1. The decoder is the active CD player, that can process separation only on mix signals that have been generated by the coder. At the coder, the mix signal is generated as a linear instantaneous stationary stereo (LISS) mixture, i.e. summation of source signals with constant-gain panning coeﬃcients. Then, the system looks for the two sources that better “explain” the mixture (i.e. the two source signals that are predominant in the mix signal) at diﬀerent time intervals and frequency channels, and the corresponding source indexes are embedded into the mixture signal as side-information using watermarking. The watermarked mix signal is then quantized to 16-bits PCM. At the decoder, the only available signal is the watermarked and quantized mix signal. The side-information is extracted from the mix signal and used to separate the source signals by a local time / frequency mixture inversion process. 2.1

Time-Frequency Decomposition

The voice / instrument source signals are non-stationary, with possibly large temporal and spectral variability, and they generally strongly overlap in the time domain. Decomposing the signals in the time-frequency (TF) domain leads to a sparse representation, i.e. few TF coeﬃcients have a high energy and the overlapping of signals is much lower in the TF domain than in the time domain

Interactive Music with Active Audio CDs

s1[n]

MDCT

s2[n]

MDCT

sI[n]

MDCT

xL[n] Mixing (LIS S )

xR[n]

MDCT MDCT

S1[f,t]

Ora cle e s tima tor

SI[f,t]

Ift

Coding

XL[f,t]

XLW[ f ,t]

XR[f,t]

Wa te rma rking XRW[ f ,t]

IMDCT

16-bits P CM

IMDCT

16-bits P CM

Mixing ma trix A

~ xLW [n]

~ xRW [n]

MDCT MDCT

Sˆ1[ f ,t]

~ XLW[ f ,t] ~ XRW[ f , t]

33

Wa te rma rk e xtra ction

Ift De coding A

2×2 inve rs ion

SˆI [ f , t]

~ xLW [n] ~ x RW [ n ]

IMDCT

sˆ1[ n ]

IMDCT

sˆ2 [ n]

IMDCT

sˆI [ n]

Fig. 1. Informed source separation coder and decoder

[29][7][15][20]. Therefore, the separation of source signals can be carried out more eﬃciently in the TF domain. The Modiﬁed Discrete Cosine Transform (MDCT) [21] is used as the TF decomposition since it presents several properties very suitable for the present problem: good energy concentration (hence emphasizing audio signals sparsity), very good robustness to quantization (hence robustness to quantization-based watermarking), orthogonality and perfect reconstruction. Detailed description of the MDCT equations are not provided in the present paper, since it can be found in many papers, e.g. [21]. The MDCT is applied on the source signals and on the mixture signal at the input of the coder to enable the selection of predominant sources in the TF domain. Watermarking of the resulting side-information is applied on the MDCT coeﬃcients of the mix signal and the time samples of the watermarked mix signal are provided by inverse MDCT (IMDCT). At the decoder, the (PCM-quantized) mix signal is MDCTtransformed and the side-information is extracted from the resulting coeﬃcients. Source separation is also carried out in the MDCT domain, and the resulting separated MDCT coeﬃcients are used to reconstruct the corresponding timedomain separated source signals by IMDCT. Technically, the MDCT / IMDCT is applied on signal time frames of W = 2048 samples (46.5ms for a sampling frequency fs = 44.1kHz), with a 50%-overlap between consecutive frames (of 1024 frequency bins). The frame length W is chosen to follow the dynamics of music signals while providing a frequency resolution suitable for the separation. Appropriate windowing is applied at both analysis and synthesis to ensure the “perfect reconstruction” property [21].

34

S. Marchand, B. Mansencal, and L. Girin

2.2

Informed Source Separation

Since the MDCT is a linear transform, the LISS source separation problem remains LISS in the transformed domain. For each frequency bin f and time bin t, we thus have: X(f, t) = A · S(f, t) (1) where X(f, t) = [X1 (f, t), X2 (f, t)]T denotes the stereo mixture coeﬃcients vector and S(f, t) = [S1 (f, t), · · · , SN (f, t)]T denotes the N -source coeﬃcients vector. Because of audio signal sparsity in the TF domain, only at most 2 sources are assumed to be relevant, i.e. of signiﬁcant energy, at each TF bin (f, t). Therefore, the mixture is locally given by: X(f, t) ≈ AIf t SIf t (f, t)

(2)

where If t denotes the set of 2 relevant sources at TF bin (f, t). AIf t represents the 2 × 2 mixing sub-matrix made with the Ai columns of A, i ∈ If t . If I f t denotes the complementary set of non-active (or at least poorly active) sources at TF bin (f, t), the source signals at bin (f, t) are estimated by [7]: ˆ I (f, t) = A−1 X(f, t) S ft If t (3) ˆ (f, t) = 0 S Ift where A−1 If t denotes the inverse of AIf t . Note that such a separation technique exploits the 2-channel spatial information of the mixture signal and relaxes the restrictive assumption of a single active source at each TF bin, as made in [29][2][3]. The side-information that is transmitted between coder and decoder (in addition to the mix signal) mainly consists of the coeﬃcients of the mixing matrix A and the combination of indexes If t that identiﬁes the predominant sources in each TF bin. This contrasts with classic blind and semi-blind separation methods where those both types of information have to be estimated from the mix signal only, generally in two steps which can both be a very challenging task and source of signiﬁcant errors. As for the mixing matrix, the number of coeﬃcients to be transmitted is quite low in the present LISS conﬁguration2 . Therefore, the transmission cost of A is negligible compared to the transmission cost of If t , and it occupies a very small portion of the watermarking capacity. As for the source indexes, If t is determined at the coder for each TF bin using the source signals, the mixture signal, and the mixture matrix A, as the combination that provides the lower mean squared error (MSE) between the original source signals and the estimated source signals obtained with Equation (3) (see [17] for details). This process follows the line of oracle estimators, as introduced in [26] for the general purpose of evaluating the performances of source 2

For 5-source signals, if A is made of normalized column vectors depending on source azimuths, then we have only 5 coeﬃcients.

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35

separation algorithms, especially in the case of underdetermined mixtures and sparse separation techniques. Note that because of the orthogonality / perfect reconstruction property of the MDCT, the selection of the optimal source combination can be processed separately at each TF bin, in spite of the overlap-add operation at source signal reconstruction by IMDCT [26]. When the number of sources is reasonable (typically about 5 for a standard western popular music song), I˜f t can be found by exhaustive search, since in contrast to the decoding process, the encoding process is done oﬄine and is therefore not subdue to real-time constraints. It is important to note that at the coder, the optimal combination is determined from the “original” (unwatermarked) mix signal. In contrast, at the decoder, only the watermarked mix signal is available, and the source separation is obtained by applying Equation (3) using the MDCT coeﬃcients of the water˜ W (f, t) instead of the MDCT marked (and 16-bit PCM quantized) mix signal X coeﬃcients of the original mix signal X(f, t). However, it has been shown in [17] that the inﬂuence of the watermarking (and PCM quantization) process on separation performance is negligible. This is because the optimal combination for each TF bin can be coded with a very limited number of bits before being embedded into the mixture signal. For example, for a 5-source mixture, the number of combinations of two sources among ﬁve is 10 and a 4-bit ﬁxed-size code is appropriate (although non optimal) for encoding If t . In practice, the source separation process can be limited to the [0; 16]kHz bandwidth, because the energy of audio signals is generally very low beyond 16kHz. Since the MDCT decomposition provides as many coeﬃcients as time samples, the side-information bit-rate is 4×Fs ×16, 000/(Fs/2) = 128kbits/s (Fs = 44, 1kHz is the sampling frequency), which can be split in two 64kbits/s streams, one for each of the stereo channels. This is about 1/4 of the maximum capacity of the watermarking process (see below), and for such capacity, the distortion of the MDCT coeﬃcients by the watermarking process is suﬃciently low to not corrupt the separation process of Equation (3). In fact, the main source of degradation in the separation process relies in the sparsity assumption, i.e. the fact that “residual” non-predominant, but non-null, sources may interfere as noise in the local inversion process. Separation performances are described in details in [17] for “real-world” 5source LISS music mixtures of diﬀerent musical styles. Basically, source enhancement from input (mix) to output (separated) ranges from 17dB to 25dB depending on sources and mixture, which is remarkable given the diﬃculty of such underdetermined mixtures. The rejection of competing sources is very eﬃcient and the source signals are clearly isolated, as conﬁrmed by listening tests. Artefacts (musical noise) are present but are quite limited. The quality of the isolated source signals makes them usable for individual manipulation by the spatializer. 2.3

Watermarking Process

The side-information embedding process is derived from the Quantization Index Modulation (QIM) technique of [8], applied to the MDCT coeﬃcients of the

36

S. Marchand, B. Mansencal, and L. Girin

w

X (t, f ) ΔQIM X(t, f )

Δ(t, f )

00

01

11

10 11 01 00 10 11 01 00 10 11 01 00 10 11 01 00 10 11 01 00

10

Fig. 2. Example of QIM using a set of quantizers for C(t, f ) = 2 and the resulting global grid. We have Δ(t, f ) = 2C(t,f ) · ΔQIM . The binary code 01 is embedded into the MDCT coeﬃcient X(t, f ) by quantizing it to X w (t, f ) using the quantizer indexed by 01.

mixture signal in combination with the use of a psycho-acoustic model (PAM) for the control of inaudibility. It has been presented in details in [19][18]. Therefore, we just present the general lines of the watermarking process in this section, and we refer the reader to these papers for technical details. The embedding principle is the following. Let us denote by C(t, f ) the capacity at TF bin (t, f ), i.e. the maximum size of the binary code to be embedded in the MDCT coeﬃcient at that TF bin (under inaudibility constraint). We will see below how C(t, f ) is determined for each TF bin. For each TF bin (t, f ), a set of 2C(t,f ) uniform quantizers is deﬁned, which quantization levels are intertwined, and each quantizer represents a C(t, f )-bit binary code. Embedding a given binary code on a given MDCT coeﬃcient is done by quantizing this coefﬁcient with the corresponding quantizer (i.e. the quantizer indexed by the code to transmit; see Fig. 2). At the decoder, recovering the code is done by comparing the transmitted MDCT coeﬃcient (potentially corrupted by transmission noise) with the 2C(t,f ) quantizers, and selecting the quantizer with the quantization level closest to the transmitted MDCT coeﬃcient. Note that because the capacity values depend on (f, t), those values must be transmitted to the decoder to select the right set of quantizers. For this, a ﬁxed-capacity embedding “reservoir” is allocated in the higher frequency region of the spectrum, and the

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37

capacity values are actually deﬁned within subbands (see [18] for details). Note also that the complete binary message to transmit (here the set of If t codes) is split and spread across the diﬀerent MDCT coeﬃcients according to the local capacity values, so that each MDCT coeﬃcient carries a small part of the complete message. Conversely, the decoded elementary messages have to be concatenated to recover the complete message. The embedding rate R is given by the average total number of embedded bits per second of signal. It is obtained by summing the capacity C(t, f ) over the embedded region of the TF plane and dividing the result by the signal duration. The performance of the embedding process is determined by two related constraints: the watermark decoding must be robust to the 16-bit PCM conversion of the mix signal (which is the only source of noise because the “perfect reconstruction” property of MDCT ensures transparency of IMDCT/MDCT chained processes), and the watermark must be inaudible. The time-domain PCM quantization leads to additive white Gaussian noise on MDCT coeﬃcients, which induces a lower bound for ΔQIM the minimum distance between two diﬀerent levels of all QIM quantizers (see Fig. 2). Given that lower bound, the inaudibility constraint induces an upper bound on the number of quantizers, hence a corresponding upper bound on the capacity C(t, f ) [19][18]. More speciﬁcally, the constraint is that the power of the embedding error in the worst case remains under the masking threshold M (t, f ) provided by a psychoacoustic model. The PAM is inspired from the MPEG-AAC model [11] and adapted to the present data hiding problem. It is shown in [18] that the optimal capacity is given by: α 10 1 M (t, f ) · 10 C α (t, f ) = log +1 (4) 2 2 Δ2QIM where . denotes the floor function, and α is a scaling factor (in dB) that enables users to control the trade-oﬀ between signal degradation and embedding rate by translating the masking threshold. Signal quality is expected to decrease as embedding rate increases, and vice-versa. When α > 0dB, the masking threshold is raised. Larger values of the quantization error allow for larger capacities (and thus higher embedding rate), at the price of potentially lower quality. At the opposite, when α < 0dB, the masking threshold is lowered, leading to a “safety margin” for the inaudibility of the embedding process, at the price of lower embedding rate. It can be shown that the embedding rate Rα corresponding to C α and the basic rate R = R0 are related by [18]: Rα R + α ·

log2 (10) · Fu 10

(5)

(Fu being the bandwidth of the embedded frequency region). This linear relation enables to easily control the embedding rate by the setting of α. The inaudibility of the watermarking process has been assessed by subjective and objective tests. In [19][18], Objective Diﬀerence Grade (ODG) scores [24][12] were calculated for a large range of embedding rates and diﬀerent musical styles. ODG remained very close to zero (hence imperceptibility of the watermark)

38

S. Marchand, B. Mansencal, and L. Girin

for rates up to about 260kbps for musical styles such as pop, rock, jazz, funk, bossa, fusion, etc. (and “only” up to about 175kbps for classical music). Such rates generally correspond to the basic level of the masking curve allowed by the PAM (i.e. α = 0dB). More “comfortable” rates can be set between 150 and 200kbits/s to guarantee transparent quality for the embedded signal. This ﬂexibility is used in our informed source separation system to ﬁt the embedding capacity with the bit-rate of the side-information, which is at the very reasonable value of 64kbits/s/channel. Here, the watermarking is guaranteed to be “highly inaudible”, since the masking curve is signiﬁcantly lowered to ﬁt the required capacity.

3

Sound Spatialization

Now that we have recovered the diﬀerent sound sources present in the original mix, we can allow the user to manipulate them in space. We consider each punctual and omni-directional sound source in the horizontal plane, located by its (ρ, θ) coordinates, where ρ is the distance of the source to the head center and θ is the azimuth angle. Indeed, as a ﬁrst approximation in most musical situations, both the listeners and instrumentalists are standing on the (same) ground, with no relative elevation. Moreover, we consider that the distance ρ is large enough for the acoustic wave to be regarded as planar when reaching the ears. 3.1

Acoustic Cues

In this section, we intend to perform real-time high-quality (convolutive) mixing. The source s will reach the left (L) and right (R) ears through diﬀerent acoustic paths, characterizable with a pair of ﬁlters, which spectral versions are called Head-Related Transfer Functions (HRTFs). HRTFs are frequency- and subjectdependent. The CIPIC database [1] samples diﬀerent listeners and directions of arrival. A sound source positioned to the left will reach the left ear sooner than the right one, in the same manner the right level should be lower due to wave propagation and head shadowing. Thus, the diﬀerence in amplitude or Interaural Level Diﬀerence (ILD, expressed in decibels – dB) [23] and diﬀerence in arrival time or Interaural Time Diﬀerence (ITD, expressed in seconds) [22] are the main spatial cues for the human auditory system [6]. Interaural Level Diﬀerences. After Viste [27], the ILDs can be expressed as functions of sin(θ), thus leading to a sinusoidal model: ILD(θ, f ) = α(f ) sin(θ)

(6)

where α(f ) is the average scaling factor that best suits our model, in the leastsquare sense, for each listener of the CIPIC database (see Fig. 3). The overall error of this model over the CIPIC database for all subjects, azimuths, and frequencies is of 4.29dB.

Interactive Music with Active Audio CDs

39

level scaling factor

α

40

30

20

10

time scaling factor

β

0

0

2

4

6

0

2

4

6

8

10

12

14

16

18

20

8

10

12

14

16

18

20

4 3 2 1 0

Frequency [kHz] Fig. 3. Frequency-dependent scaling factors: α (top) and β (bottom)

Interaural Time Diﬀerences. Because of the head shadowing, Viste uses for the ITDs a model based on sin(θ) + θ, after Woodworth [28]. However, from the theory of the diﬀraction of an harmonic plane wave by a sphere (the head), the ITDs should be proportional to sin(θ). Contrary to the model by Kuhn [13], our model takes into account the inter-subject variation and the full-frequency band. The ITD model is then expressed as: ITD(θ, f ) = β(f )r sin(θ)/c

(7)

where β is the average scaling factor that best suits our model, in the leastsquare sense, for each listener of the CIPIC database (see Fig. 3), r denotes the head radius, and c is the sound celerity. The overall error of this model over the CIPIC database is 0.052ms (thus comparable to the 0.045ms error of the model by Viste). Distance Cues. In ideal conditions, the intensity of a source is halved (decreases by −6dB) when the distance is doubled, according to the well-known Inverse Square Law [5]. Applying only this frequency-independent rule to a signal has no eﬀect on the sound timbre. But when a source moves far from the listener, the high frequencies are more attenuated than the low frequencies. Thus

40

S. Marchand, B. Mansencal, and L. Girin

the sound spectrum changes with the distance. More precisely, the spectral centroid moves towards the low frequencies as the distance increases. In [4], the authors show that the frequency-dependent attenuation due to atmospheric attenuation is roughly proportional to f 2 , similarly to the ISO 9613-1 norm [10]. Here, we manipulate the magnitude spectrum to simulate the distance between the source and the listener. Conversely, we would measure the spectral centroid (related to brightness) to estimate the source’s distance to listener. In a concert room, the distance is often simulated by placing the speaker near / away from the auditorium, which is sometimes physically restricted in small rooms. In fact, the architecture of the room plays an important role and can lead to severe modiﬁcations in the interpretation of the piece. Here, simulating the distance is a matter of changing the magnitude of each short-term spectrum X. More precisely, the ISO 9613-1 norm [10] gives the frequency-dependent attenuation factor in dB for given air temperature, humidity, and pressure conditions. At distance ρ, the magnitudes of X(f ) should be attenuated by D(f, ρ) decibels: D(f, ρ) = ρ · a(f )

(8)

where a(f ) is the frequency-dependent attenuation, which will have an impact on the brightness of the sound (higher frequencies being more attenuated than lower ones). More precisely, the total absorption in decibels per meter a(f ) is given by a rather complicated formula: 12 − 52 a(f ) ≈ 8.68 · F 2 1.84 · 10−11 TT0 P0 + TT0 P

0.01275 · e−2239.1/T /[Fr,O + (F 2 /Fr,O )] +0.1068 · e−3352/T /[Fr,N + (F 2 /Fr,N )] (9) where F = f /P , Fr,O = fr,O /P , Fr,N = fr,N /P are frequencies scaled by the atmospheric pressure P , and P0 is the reference atmospheric pressure (1 atm), f is the frequency in Hz, T is the atmospheric temperature in Kelvin (K), T0 is the reference atmospheric temperature (293.15K), fr,O is the relaxation frequency of molecular oxygen, and fr,N is the relaxation frequency of molecular nitrogen (see [4] for details). The spectrum thus becomes: X(ρ, f ) = 10(XdB (t,f )−D(f,ρ))/20

(10)

where XdB is the spectrum X in dB scale. 3.2

Binaural Spatialization

In binaural listening conditions using headphones, the sound from each earphone speaker is heard only by one ear. Thus the encoded spatial cues are not aﬀected by any cross-talk signals between earphone speakers.

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41

To spatialize a sound source to an expected azimuth θ, for each short-term spectrum X, we compute the pair of left (XL ) and right (XR ) spectra from the spatial cues corresponding to θ, using Equations (6) and (7), and: XL (t, f ) = HL (t, f )X(t, f ) with HL (t, f ) = 10+Δa (f )/2 e+jΔφ (f )/2 ,

(11)

−Δa (f )/2 −jΔφ (f )/2

(12)

XR (t, f ) = HR (t, f )X(t, f ) with HR (t, f ) = 10

e

(because of the symmetry among the left and right ears), where Δa and Δφ are given by: Δa (f ) = ILD(θ, f )/20,

(13)

Δφ (f ) = ITD(θ, f ) · 2πf.

(14)

This is indeed a convolutive model, the convolution turning into a multiplication in the spectral domain. Moreover, the spatialization coeﬃcients are complex. The control of both amplitude and phase should provide better audio quality [25] than amplitude-only spatialization. Indeed, we reach a remarkable spatialization realism through informal listening tests with AKG K240 Studio headphones. 3.3

Multi-loudspeaker Spatialization

In a stereophonic display, the sound from each loudspeaker is heard by both ears. Thus, as in the transaural case, the stereo sound reaches the ears through four acoustic paths, corresponding to transfer functions (Cij , i representing the speaker and j the ear), see Fig. 4. Here, we generate these paths artiﬁcially using the binaural model (using the distance and azimuth of the source to the ears for H, and of the speakers to the ears for C). Since we have: XL = HL X = CLL KL X +CLR KR X

YL

YR

XR = HR X = CRL KL X +CRR KR X

YL

(15) (16)

YR

the best panning coeﬃcients under CIPIC conditions for the pair of speakers to match the binaural signals at the ears (see Equations (11) and (12)) are then given by: KL (t, f ) = C · (CRR HL − CLR HR ) , (17) KR (t, f ) = C · (−CRL HL + CLL HR )

(18)

with the determinant computed as: C = 1/ (CLL CRR − CRL CLR ) .

(19)

During diﬀusion, the left and right signals (YL , YR ) to feed left and right speakers are obtained by multiplying the short-term spectra X with KL and KR , respectively: YL (t, f ) = KL (t, f )X(t, f ) = C · (CRR XL − CLR XR ) ,

(20)

YR (t, f ) = KR (t, f )X(t, f ) = C · (−CRL XL + CLL XR ) .

(21)

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S. Marchand, B. Mansencal, and L. Girin X

KL

KR

YL

YR sound image

SL

SR HR

C LL

R L

CL

XL

R

CRR

HL C

XR

Fig. 4. Stereophonic loudspeaker display: the sound source X reaches the ears L, R through four acoustic paths (CLL , CLR , CRL , CRR )

sound image

S2

S3

L

S

R

S1

S4

Fig. 5. Pairwise paradigm: for a given sound source, signals are dispatched only to the two speakers closest to it (in azimuth)

In a setup with many speakers we use the classic pair-wise paradigm [9], consisting in choosing for a given source only the two speakers closest to it (in

Interactive Music with Active Audio CDs

player sources file separator reader 2 channels

active CD

N output ports

43

spatializer N sources N input ports

... M output ports

M speakers

Fig. 6. Overview of the software system architecture

azimuth): one at the left of the source, the other at its right (see Fig. 5). The left and right signals computed for the source are then dispatched accordingly.

4

Software System

Our methods for source separation and sound spatialization have been implemented as a real-time software system, programmed in C++ language and using Qt43 , JACK4 , and FFTW5 . These libraries were chosen to ensure portability and performance on multiple platforms. The current implementation has been tested on Linux and MacOS X operating systems, but should work with very minor changes on other platforms, e.g. Windows. Fig. 6 shows an overview of the architecture of our software system. Source separation and sound spatialization are implemented as two diﬀerent modules. We rely on JACK audio ports system to route audio streams between these two modules in real time. This separation in two modules was mainly dictated by a diﬀerent choice of distribution license: the source separation of the active player should be patented and released without sources, while the spatializer will be freely available under the GNU General Public License. 4.1

Usage

Player. The active player is presented as a simple audio player, based on JACK. The graphical user interface (GUI) is a very common player interface. It allows to play or pause the reading / decoding. The player reads “activated” stereo ﬁles, from an audio CD or ﬁle, and then decodes the stereo mix in order to extract the N (mono) sources. Then these sources are transferred to N JACK output ports, currently named QJackPlayerSeparator:outputi, with i in [1; N ]. Spatializer. The spatializer is also a real-time application, standalone and based on JACK. It has N inputs ports that correspond to the N sources to spatialize. These ports are to be connected, with the JACK ports connection system, to the N outputs ports of the active player. The spatializer can be conﬁgured to work with headphones (binaural conﬁguration) or with M loudspeakers. 3 4 5

See URL: http://trolltech.com/products/qt See URL: http://jackaudio.org See URL: http://www.fftw.org

44

S. Marchand, B. Mansencal, and L. Girin

Fig. 7. From the stereo mix stored on the CD, our player is allowing the listener (center ) to manipulate 5 sources in the acoustic space, using here an octophonic display (top) or headphones (bottom)

Fig. 7 shows the current interface of the spatializer, which displays a bird’s eye view of the audio scene. The user’s avatar is in the middle, represented by a head viewed from above. He is surrounded by various sources, represented as

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45

Fig. 8. Example of conﬁguration ﬁles: 5-source conﬁguration (top), binaural output conﬁguration (middle), and then 8-speaker conﬁguration (bottom) ﬁles

notes in colored discs. When used in a multi-speaker conﬁguration, speakers may be represented in the scene. If used in a binaural conﬁguration, the user’s avatar is represented wearing headphones. With this graphical user interface, the user can interactively move each source individually. He picks one of the source representation and drags it around. The corresponding audio stream is then spatialized, in real time, according to the new source position (distance and azimuth). The user can also move his avatar among the sources, as if the listener was moving on the stage, between the instrumentalists. In this situation, the spatialization changes for all the sources simultaneously, according to their new relative positions to the moving user avatar. Inputs and outputs are set via two conﬁguration ﬁles (see Fig. 8). A source conﬁguration ﬁle deﬁnes the number of sources. For each source, this ﬁle gives the name of the output port to which a spatializer input port will be connected, and also its original azimuth and distance. Fig. 8 shows the source conﬁguration ﬁle to connect to the active player with 5 ports. A speaker conﬁguration ﬁle deﬁnes the number of speakers. For each speaker, this ﬁle gives the name of the physical (soundcard) port to which a spatializer output port will be connected, and the azimuth and distance of the speaker. The binaural case is distinguished

46

S. Marchand, B. Mansencal, and L. Girin

sources

speakers

Fig. 9. Processing pipeline for the spatialization of N sources on M speakers

by the fact that it has only two speakers with neither azimuth nor distance speciﬁed. Fig. 8 shows the speaker conﬁguration ﬁles for binaural and octophonic (8-speaker) conﬁguration. 4.2

Implementation

Player. The current implementation is divided into three threads. The main thread is the Qt GUI. A second thread reads and buﬀerizes data from the stereo ﬁle, to be able to compensate for any physical CD reader latency. The third thread is the JACK process function. It separates the data for the N sources and feeds the output ports accordingly. In the current implementation, the number of output sources is ﬁxed to N = 5. Our source separation implementation is rather eﬃcient as for a Modiﬁed Discrete Cosine Transform (MDCT) of W samples, we only do a Fast Fourier Transform (FFT) of size W/4. Indeed, a MDCT of length W is almost equivalent to a type-IV DCT of length W/2 that can be computed with a FFT of length W/4. Thus, as we use MDCT and IMDCT of size W = 2048, we only do FFT and IFFT of 512 samples. Spatializer. The spatializer is currently composed of two threads: a main thread, the Qt GUI, and the JACK process function. Fig. 9 shows the processing pipeline for the spatialization. For each source, xi is ﬁrst transformed into the spectral domain with a FFT to obtain its spectrum Xi . This spectrum is attenuated for distance ρi (see Equation (10)). Then, for an azimuth θi , we obtain the left (XiL ) and right (XiR ) spectra (see Equations

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47

(11) and (12)). The dispatcher then chooses the pair (j, j + 1) of speakers surrounding the azimuth θi , transforms the spectra XiL and XiR by the coeﬃcients corresponding to this speaker pair (see Equations (20) and (21)), and adds the resulting spectra Yj and Yj+1 in the spectra of these speakers. Finally, for each speaker, its spectrum is transformed with an IFFT to obtain back in the time domain the mono signal yj for the corresponding output. Source spatialization is more computation-intensive than source separation, mainly because it requires more transforms (N FFTs and M IFFTs) of larger size W = 2048. For now, source spatialization is implemented as a serial process. However, we can see that this pipeline is highly parallel. Indeed, almost everything operates on separate data. Only the spectra of the speakers may be accessed concurrently, to accumulate the spectra of sources that would be spatialized to the same or neighbouring speaker pairs. These spectra should then be protected with mutual exclusion mechanisms. A future version will take advantage of multi-core processor architectures. 4.3

Experiments

Our current prototype has been tested on an Apple MacBook Pro, with an Intel Core 2 Duo 2.53GHz processor, connected to headphones or to a 8-speaker system, via a MOTU 828 MKII soundcard. For such a conﬁguration, the processing power is well contained. In order to run in real time, given a signal sampling

Fig. 10. Enhanced graphical interface with pictures of instruments for sources and propagating sound waves represented as colored circles

48

S. Marchand, B. Mansencal, and L. Girin

frequency of 44.1kHz and windows of 2048 samples, the overall processing time should be less than 23ms. With our current implementation, 5-source separation and 8-speaker spatialization, this processing time is in fact less than 3ms on the laptop mentioned previously. Therefore, the margin to increase the number of sources to separate and/or the number of loudspeakers is quite confortable. To conﬁrm this, we exploited the split of the source separation and spatialization modules to test the spatializer without the active player, since the latter is currently limited to 5 sources. We connected to the spatializer a multi-track player that reads several ﬁles simultaneously and exposes these tracks as JACK output ports. Tests showed that the spatialization can be applied to roughly 48 sources on 8 speakers, or 40 sources on 40 speakers on this computer. These performances allow us to have some processing power for other computations, to improve user experience for example. Fig. 10 shows an example of an enhanced graphical interface where the sources are represented with pictures of the instruments, and the propagation of the sound waves is represented for each source by time-evolving colored circles. The color of each circle is computed from the color (spectral envelope) of the spectrum of each source and updated in real time as the sound changes.

5

Conclusion and Future Work

We have presented a real-time system for musical interaction from stereo ﬁles, fully backward-compatible with standard audio CDs. This system consists of a source separator and a spatializer. The source separation is based on the sparsity of the source signals in the spectral domain and the exploitation of the stereophony. This system is characterized by a quite simple separation process and by the fact that some sideinformation is inaudibly embedded in the signal itself to guide the separation process. Compared to (semi-)blind approaches also based on sparsity and local mixture inversion, the informed aspect of separation guarantees the optimal combination of the sources, thus leading to a remarkable increase of quality of the separated signals. The sound spatialization is based on a simpliﬁed model of the head-related transfer functions, generalized to any multi-loudspeaker conﬁguration using a transaural technique for the best pair of loudspeaker for each sound source. Although this quite simple technique does not compete with the 3D accuracy of Ambisonics or holophony (Wave Field Synthesis), it is very ﬂexible (no speciﬁc loudspeaker conﬁguration) and suitable for a large audience (no hot-spot eﬀect) with suﬃcient sound quality. The resulting software system is able to separate 5-source stereo mixtures (read from audio CD or 16-bit PCM ﬁles) in real time and it enables the user to remix the piece of music during restitution with basic functions such as volume and spatialization control. The system has been demonstrated in several countries with excellent feedback from the users / listeners, with a clear potential in terms of musical creativity, pedagogy, and entertainment.

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For now, the mixing model imposed by the informed source separation is generally over-simplistic when professional / commercial music production is at stake. Extending the source separation technique to high-quality convolutive mixing is part of our future research. As shown in [14], the model we use for the spatialization is more general, and can be used as well to localize audio sources. Thus we would like to add the automatic detection of the speaker conﬁguration to our system, from a pair of microphones placed in the audience, as well as the automatic ﬁne tuning of the spatialization coeﬃcients to improve the 3D sound eﬀect. Regarding performance, lots of operations are on separated data and thus could easily be parallelized on modern hardware architectures. Last but not least, we are also porting the whole application to mobile touch devices, such as smart phones and tablets. Indeed, we believe that these devices are perfect targets for a system in between music listening and gaming, and gestural interfaces with direct interaction to move the sources are very intuitive.

Acknowledgments This research was partly supported by the French ANR (Agence Nationale de la Recherche) DReaM project (ANR-09-CORD-006).

References 1. Algazi, V.R., Duda, R.O., Thompson, D.M., Avendano, C.: The CIPIC HRTF database. In: Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), New Paltz, New York, pp. 99–102 (2001) 2. Araki, S., Sawada, H., Makino, S.: K-means based underdetermined blind speech separation. In: Makino, S., et al. (eds.) Blind Source Separation, pp. 243–270. Springer, Heidelberg (2007) 3. Araki, S., Sawada, H., Mukai, R., Makino, S.: Underdetermined blind sparse source separation for arbitrarily arranged multiple sensors. Signal Processing 87(8), 1833– 1847 (2007) 4. Bass, H., Sutherland, L., Zuckerwar, A., Blackstock, D., Hester, D.: Atmospheric absorption of sound: Further developments. Journal of the Acoustical Society of America 97(1), 680–683 (1995) 5. Berg, R.E., Stork, D.G.: The Physics of Sound, 2nd edn. Prentice Hall, Englewood Cliﬀs (1994) 6. Blauert, J.: Spatial Hearing. revised edn. MIT Press, Cambridge (1997); Translation by J.S. Allen 7. Boﬁll, P., Zibulevski, M.: Underdetermined blind source separation using sparse representations. Signal Processing 81(11), 2353–2362 (2001) 8. Chen, B., Wornell, G.: Quantization index modulation: A class of provably good methods for digital watermarking and information embedding. IEEE Transactions on Information Theory 47(4), 1423–1443 (2001) 9. Chowning, J.M.: The simulation of moving sound sources. Journal of the Acoustical Society of America 19(1), 2–6 (1971)

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10. International Organization for Standardization, Geneva, Switzerland: ISO 96131:1993: Acoustics – Attenuation of Sound During Propagation Outdoors – Part 1: Calculation of the Absorption of Sound by the Atmosphere (1993) 11. ISO/IEC JTC1/SC29/WG11 MPEG: Information technology Generic coding of moving pictures and associated audio information Part 7: Advanced Audio Coding (AAC) IS13818-7(E) (2004) 12. ITU-R: Method for objective measurements of perceived audio quality (PEAQ) Recommendation BS1387-1 (2001) 13. Kuhn, G.F.: Model for the interaural time diﬀerences in the azimuthal plane. Journal of the Acoustical Society of America 62(1), 157–167 (1977) 14. Mouba, J., Marchand, S., Mansencal, B., Rivet, J.M.: RetroSpat: a perceptionbased system for semi-automatic diﬀusion of acousmatic music. In: Proceedings of the Sound and Music Computing (SMC) Conference, Berlin, pp. 33–40 (2008) 15. O’Grady, P., Pearlmutter, B.A., Rickard, S.: Survey of sparse and non-sparse methods in source separation. International Journal of Imaging Systems and Technology 15(1), 18–33 (2005) 16. Parvaix, M., Girin, L.: Informed source separation of underdetermined instantaneous stereo mixtures using source index embedding. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Dallas, Texas (2010) 17. Parvaix, M., Girin, L.: Informed source separation of linear instantaneous underdetermined audio mixtures by source index embedding. IEEE Transactions on Audio, Speech, and Language Processing (accepted, pending publication, 2011) 18. Pinel, J., Girin, L., Baras, C.: A high-rate data hiding technique for uncompressed audio signals. IEEE Transactions on Audio, Speech, and Language Processing (submitted) 19. Pinel, J., Girin, L., Baras, C., Parvaix, M.: A high-capacity watermarking technique for audio signals based on MDCT-domain quantization. In: International Congress on Acoustics (ICA), Sydney, Australia (2010) 20. Plumbley, M.D., Blumensath, T., Daudet, L., Gribonval, R., Davies, M.E.: Sparse representations in audio and music: From coding to source separation. Proceedings of the IEEE 98(6), 995–1005 (2010) 21. Princen, J., Bradley, A.: Analysis/synthesis ﬁlter bank design based on time domain aliasing cancellation. IEEE Transactions on Acoustics, Speech, and Signal Processing 64(5), 1153–1161 (1986) 22. Strutt (Lord Rayleigh), J.W.: Acoustical observations i. Philosophical Magazine 3, 456–457 (1877) 23. Strutt (Lord Rayleigh), J.W.: On the acoustic shadow of a sphere. Philosophical Transactions of the Royal Society of London 203A, 87–97 (1904) 24. Thiede, T., Treurniet, W., Bitto, R., Schmidmer, C., Sporer, T., Beerends, J., Colomes, C.: PEAQ - the ITU standard for objective measurement of perceived audio quality. Journal of the Audio Engineering Society 48(1), 3–29 (2000) 25. Tournery, C., Faller, C.: Improved time delay analysis/synthesis for parametric stereo audio coding. Journal of the Audio Engineering Society 29(5), 490–498 (2006) 26. Vincent, E., Gribonval, R., Plumbley, M.D.: Oracle estimators for the benchmarking of source separation algorithms. Signal Processing 87, 1933–1950 (2007) ´ 27. Viste, H.: Binaural Localization and Separation Techniques. Ph.D. thesis, Ecole Polytechnique F´ed´erale de Lausanne, Switzerland (2004) 28. Woodworth, R.S.: Experimental Psychology. Holt, New York (1954) 29. Yılmaz, O., Rickard, S.: Blind separation of speech mixtures via time-frequency masking. IEEE Transactions on Signal Processing 52(7), 1830–1847 (2004)

Pitch Gestures in Generative Modeling of Music Kristoffer Jensen Aalborg University Esbjerg, Niels Bohr Vej 8, 6700 Esbjerg, Denmark [email protected]

Abstract. Generative models of music are in need of performance and gesture additions, i.e. inclusions of subtle temporal and dynamic alterations, and gestures so as to render the music musical. While much of the research regarding music generation is based on music theory, the work presented here is based on the temporal perception, which is divided into three parts, the immediate (subchunk), the short-term memory (chunk), and the superchunk. By review of the relevant temporal perception literature, the necessary performance elements to add in the metrical generative model, related to the chunk memory, are obtained. In particular, the pitch gestures are modeled as rising, falling, or as arches with positive or negative peaks. Keywords: gesture; human cognition; perception; chunking; music generation.

1 Introduction Music generation has more and more uses in today’s media. Be it in computer games, interactive music performances, or in interactive films, the emotional effect of the music is primordial in the appreciation of the media. While traditionally, the music has been generated in pre-recorded loops that is mixed on-the-fly, or recorded in traditional orchestras, the better understanding and models of generative music is believed to push the interactive generative music into the multimedia. Papadopoulos and Wiggins (1999) gave an early overview of the methods of algorithmic composition, deploring “that the music that they produce is meaningless: the computers do not have feelings, moods or intentions”. While vast progress has been made in the decade since this statement, there is still room for improvement. The cognitive understanding of musical time perception is the basis of the work presented here. According to Kühl (2007), this memory can be separated into three time-scales, the short, microtemporal, related to microstructure, the mesotemporal, related to gesture, and the macrotemporal, related to form. These time-scales are named (Kühl and Jensen 2008) subchunk, chunk and superchunk, and subchunks extend from 30 ms to 300 ms; the conscious mesolevel of chunks from 300 ms to 3 sec; and the reflective macrolevel of superchunks from 3 sec to roughly 30−40 sec. The subchunk is related to individual notes, the chunk to meter and gesture, and the superchunk is related to form. The superchunk was analyzed and used for in a generative model in Kühl and Jensen (2008), and the chunks were analyzed in Jensen and Kühl (2009). Further analysis of the implications of how temporal perception is S. Ystad et al. (Eds.): CMMR 2010, LNCS 6684, pp. 51–59, 2011. © Springer-Verlag Berlin Heidelberg 2011

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related to durations and timing of existing music, and anatomic and perceptual finding from the literature is given in section 2 along with an overview of the previous work in rhythm. Section 3 presents the proposed model on the inclusion of pitch gestures in music generation using statistical methods, and the section 4 discusses the integration of the pitch gesture in the generative music model. Finally, section 5 offers a conclusion.

2 Cognitive and Perceptual Aspects of Rhythm According to Snyder (2000), a beat is single point in time, while the pulse is recurring beats. Accent gives salience to beat, and meter is the organization of beats into a cyclical structure. This may or may not be different to the rhythmic grouping, which is generally seen as a phrase bounded by accented notes. Lerdahl and Jackendorff (1983) gives many examples of grouping and meter, and show how this is two independent elements; Grouping – segmentation on different levels is concerned with elements that has duration, and Meter – regular alternation of strong and weak beats is concerned with durationless elements. While grouping and meter are independent, the percept is more stable when they are congruent. The accentuation of some of the beats gives perceptually salience to the beat (Patel and Peretz 1997). This accenting can be done (Handel 1989) by for instance an intensity rise, by increasing the duration or the interval between the beats, or by increasing the frequency difference between the notes. Samson et al (2000) shows that the left temporal lobe processes rapid auditory sequences, while there are also activities in front lobe. The specialized skills related to rhythm are developed in the early years, for instance Malbrán (2000) show how 8year-old children can perform precise tapping. However, while the tapping is more precise for high tempo, drifting is ubiquitous. Gordon (1987) has determined that the perceptual attack time (PAT) is most often located at the point of the largest rise of the amplitude of the sound. However, in the experiment, the subjects had problems synchronizing many of the sounds, and Gordon concludes that the PAT is more vague for non-percussive sounds, and spectral cues may also interfere in the determination of the attack. Zwicker and Fastl (1999) introduced the notion of subjective duration, and showed that the subjective duration is longer than the objective durations for durations below 100ms. Even more subjective deviations are found, if pauses are compared to tones or noises. Zwicker and Fastl found that long sounds (above 1 second) has the same subjective durations than pauses, while shorter pauses has significantly longer subjective durations than sounds. Approximately 4 times longer for 3.2kHz tone, while 200Hz tone and white noise have approximately half the subjective duration, as compared to pauses. This is true for durations of around 100200 ms, while the difference evens out to disappear at 1sec durations. Finally Zwicker and Fastl related the subjective duration to temporal masking, and give indications that musicians would play tones shorter than indicated in order to fulfill the subjective durations of the notated music. Fraisse (1982) give an overview of his important research in rhythm perception. He states the range in which synchronization is possible to be between 200 to 1800 msec (33-300 BPM). Fraisse furthermore has analyzed classical music, and found two main durations that he calls temps longs

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(>400msec) & temps courts, and two to one ratios only found between temps longs and courts. As for natural tempo, when subjects are asked to reproduce temporal intervals, they tend to overestimate short intervals (making them longer) and underestimate long intervals (making them shorter). At an interval of about 500 msec to 600 msec, there is little over- or under-estimation. However, there are large differences across individuals, the spontaneous tempo is found to be between 1.1 to 5 taps per second, with 1.7 taps per second most occurring. There are also many spontaneous motor movements that occur at the rate of approximately 2/sec, such as walking, sucking in the newborn, and rocking. Friberg (1991), and Widmer (2002) give rules to how the dynamics and timing should be changed according to the musical position of the notes. Dynamic changes include 6db increase (doubling), and up to 100msec deviations to the duration, depending on the musical position of the notes. With these timing changes, Snyder (2000) indicate the categorical perception of beats, measures and patterns. The perception of deviations of the timing is examples of within-category distinctions. Even with large deviations from the nominal score, the notes are recognized as falling on the beats. As for melodic perception, Thomassen (1982) investigated the role of interval as melodic accents. In a controlled experiment, he modeled the anticipation using an attention span of three notes, and found that the accent perception is described ‘fairly well’. The first of two opposite frequency changes gives the strongest accentuation. Two changes in the same direction are equally effective. The larger of two changes are more powerful, as are frequency rises as compared to frequency falls.

3 Model of Pitch Gestures Music is typically composed, giving intended and inherent emotions in the structural aspects, which is then enhanced and altered by the performers, who change the dynamics, articulations, vibrato, and timing to render the music enjoyable and musical. In this work, the gestures in the pitch contour are investigated. Jensen and Kühl (2009) investigated the gestures of music through a simple model, with positive or negative slope, and with positive or negative arches, as shown in figure 1. For the songs analyzed, Jensen and Kühl found more negative than positive slopes and slightly more positive than negative arches. Huron (1996) analyzed the Essen Folk music database, and found - by averaging all melodies - positive arches. Further analyses were done by comparing the first and last note to the mean of the intermediate notes, revealing more positive than negative arches (39% and 10% respectively), and more negative than positive slopes (29% and 19% respectively). According to Thomassen (1982) the falling slopes has less powerful accents, and they would thus require less attention. The generative model is made through statistical models based on data from a musical database (The Digital Tradition 2010). From this model, note and interval occurrences are counted. These counts are then normalized, and used as probability density functions for note and intervals, respectively. This statistics are shown in figure 2. As can be seen, the intervals are not symmetrical. This corroborates the finding in Jensen and Kühl (2009) that more falling than rising slopes are found in the

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Fig. 1. Different shapes of a chunk. Positive (a-c) or negative arches (g-i), rising (a,d,g) or falling slopes (c,f,i).

Fig. 2. Note (top) and interval probability density function obtained from The Digital Tradition folk database

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pitch of music. According to Vos and Troost (1989), the smaller intervals occur more often in descending form, while the larger ones occur mainly in ascending form. However, since the slope and arch are modelled in this work, the pdf of the intervals are mirrored and added around zero, and subsequently weighted and copied back to recreate the full interval pdf. It is later made possible to create a melodic contour with a given slope and arch characteristics, as detailed below. In order to generative pitch contours with gestures, the model in figure 1 is used. For the pitch contour, only the neutral gesture (e) in figure 1, the falling and rising slope (d) and (f), and the positive and negative arches (b) and (h) are modeled here. The gestures are obtained by weighting the positive and negative slope of the interval probability density function with a weight,

pdf i = [w⋅ pdf i+ ,(1 − w)⋅ pdf i+ ].

(1)

Here, pdfi+ is the mirrored/added positive interval pdf, and w is the weight. If w=0.5, a neutral gesture is obtained, and if w<0.5, a positive slope is obtained, and if w>0.5, a negative slope is obtained. In order to obtained an arch, the value of the weight is changed to w=1- w, in the middle of the gesture. In order to obtain a musical scale, the probability density function for the intervals (pdfi) is multiplied with a suitable pdfs for the scale, such as the one illustrated in figure 2 (top),

pdf = shift( pdf i ,n0 )⋅ pdf s ⋅ wr .

(2)

As pdfs is only defined for one octave, it is circularly repeated. The interval probabilities, pdfi, are shifted for each note n0. This is done under the hypothesis that the intervals and scale notes are independent. So as to retain the register, approximately, a register weight wr is further multiplied to the pdf. This weight is one for one octave, and decreases exponentially on both sides, in order to lower the possibility of obtaining notes far from the original register. In order to obtain successive notes, the cumulative density function, cdf, is calculated from eq (2), and used to model the probability that r is less than or equal to the note intervals cdf(n0). If r is a random variable with uniform distribution in the interval (0,1), then n0 can be found as the index of the first occurrence of cdf>r. Examples of pitch contours obtained by setting w=0, and w=1, respectively, are shown in figure 3. The rising and falling pitches are reset after each gesture, in order to stay at the same register throughout the melody. The positive and negative slopes are easily recognized when listening to the resulting melodies, because of the abrupt pitch fall at the end of each gesture. The arches, in comparison, are more in need of loudness and/or brightness variations in order to make them perceptually recognized. Without this, a positive slope can be confused for a negative arch that is shifted in time, or a positive or negative slope, likewise shifted in time. Normally, an emphasis at the beginning of each gesture is sufficient for the slopes, while the arches may be in need of an emphasis at the peak of the arch as well.

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Fig. 3. Pitch contours of four melodies with positive arch, rising slope, negative arch and falling slope

4 Recreating Pitch Contours in Meter In previous work (Kühl and Jensen 2008), a generative model that produces tonal music with structures changes was presented. This model, that creates note values based on a statistical model, also introduces changes at the structural level (each 30 seconds, approximately). These changes are introduced, based on analysis of music using the musigram visualization tools (Kühl and Jensen 2008). With respect to chroma, an observation was made that only a subset of the full scale notes were used at each structural element. This subset was modified, by removing and inserting new notes from the list of possible notes, at each structural boundary. The timbre changes include varying the loudness and brightness between loud/bright and soft/dark structural elements. The main rhythm changes were based on the identification of short elements (10 seconds) with no discernable rhythm. A tempo drift of up-to 10% and insertion of faster rhythmic elements (Tatum) at structural boundaries were also identified. These structural changes were implemented in a generative model, which flowchart can be seen in figure 4. While the structural elements certainly were beneficial for the long-term interest of the music, the lack of short-term changes (chunks) and a rhythm model impeded on the quality of the music. The meter, that improves the resulting quality, is included in this generative model by adjusting the loudness and brightness of each tone according to its accent. The pitch contour is made through the model introduced in the previous section.

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Fig. 4. The generative model including meter, gesture and form. Structural changes on the note values, the intensity and the rhythm is made every 30 seconds, approximately, and gesture changes are made on average every seven notes

The notes are created using a simple envelope model and the synthesis method dubbed brightness creation function (bcf, Jensen 1999) that creates a sound with exponentially decreasing amplitudes that allows the continuous control of the brightness. The accent affects the note, so that the loudness brightness is doubled, and the duration is increased by 25 %, with 75% of the elongation made by advancing the start of the note, as found in Jensen (2010). These findings are put into a generative model of tonal music. A subset of notes (3-5) is chosen at each new form (superchunk), together with a new dynamic level. At the chunk level, new notes are created in a metrical loop, and the gestures are added to the pitch contour and used for additional gesture emphasis. Finally, at the microtemporal (subchunk) level, expressive deviations are added in order to render the loops musical. The interaction of the rigid meter with the more loose pitch gesture renders the generated notes a more musical sense, by the incertitude and the double stream that results. The pure rising and falling pitch gestures are still clearly perceptible, while the arches are less present. By setting w in eq(1) to something in between (0,1), e.g. 0.2, or 0.8, a more realistic, agreeable rising and falling gestures are resulting. Still, the arches are more natural to the ear, while the rising and falling demand more attention, in particular perhaps the rising gestures.

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5 Conclusion The automatic generation of music is in need of model to render the music expressive. This model is found using knowledge from time perception of music studies, and further studies of the cognitive and perceptual aspects of rhythm. Indeed, the generative model consists of three sources, corresponding to the immediate microtemporal, the present mesotemporal and the long-term memory macroterminal. This corresponds to the note, the gesture and the form in music. While a single stream in each of the source may not be sufficient, so far the model incorporates the macrotemporal superchunk, the metrical mesotemporal chunk and the microtemporal expressive enhancements. The work presented here has introduced gestures in the pitch contour, corresponding to the rising and falling slopes, and to the positive and negative arches, which adds a perceptual stream to the more rigid meter stream. The normal beat as is given by different researchers to be approximately 100 BPM, and Fraisse (1982) furthermore shows the existence of two main note durations, one above and one below 0.4 secs, with a ratio of two. Indications as to subjective time, given by Zwicker and Fastl (1999) are yet to be investigated, but this may well be creating uneven temporal intervals in conflict with the pulse. The inclusion of the pitch gesture model certainly, in the author’s opinion, renders the music more enjoyable, but more work remains before the generative model is ready for general-purpose uses.

References 1. Fraisse, P.: Rhythm and Tempo. In: Deutsch, D. (ed.) The Psychology of Music, 1st edn., pp. 149–180. Academic Press, New York (1982) 2. Friberg, A.: Performance Rules for Computer-Controlled Contemporary Keyboard Music. Computer Music Journal 15(2), 49–55 (1991) 3. Gordon, J.W.: The perceptual attack time of musical tones. Journal of the Acoustical Society of America, 88–105 (1987) 4. Handel, S.: Listening. MIT Press, Cambridge (1989) 5. Huron, D.: The Melodic Arch in Western Folk songs. Computing in Musicology 10, 3–23 (1996) 6. Jensen, K.: Timbre Models of Musical Sounds, PhD Dissertation, DIKU Report 99/7 (1999) 7. Jensen, K.: Investigation on Meter in Generative Modeling of Music. In: Proceedings of the CMMR, Malaga, June 21-24 (2010) 8. Jensen, K., Kühl, O.: Towards a model of musical chunks. In: Ystad, S., KronlandMartinet, R., Jensen, K. (eds.) CMMR 2008. LNCS, vol. 5493, pp. 81–92. Springer, Heidelberg (2009) 9. Kühl, O., Jensen, K.: Retrieving and recreating musical form. In: Kronland-Martinet, R., Ystad, S., Jensen, K. (eds.) CMMR 2007. LNCS, vol. 4969, pp. 270–282. Springer, Heidelberg (2008) 10. Kühl, O.: Musical Semantics. Peter Lang, Bern (2007) 11. Lerdahl, F., Jackendoff, R.: A Generative Theory of Tonal Music. The MIT Press, Cambridge (1983)

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12. Malbrán, S.: Phases in Children’s Rhythmic Development. In: Zatorre, R., Peretz, I. (eds.) The Biological Foundations of Music. Annals of the New York Academy of Sciences (2000) 13. Papadopoulos, G., Wiggins, G.: AI methods for algorithmic composition: a survey, a critical view and future prospects. In: AISB Symposium on Musical Creativity, pp. 110–117 (1999) 14. Patel, A., Peretz, I.: Is music autonomous from language? A neuropsychological appraisal. In: Deliège, I., Sloboda, J. (eds.) Perception and cognition of music, pp. 191–215. Psychology Press, Hove (1997) 15. Samson, S., Ehrlé, N., Baulac, M.: Cerebral Substrates for Musical Temporal Processes. In: Zatorre, R., Peretz, I. (eds.) The Biological Foundations of Music. Annals of the New York Academy of Sciences (2000) 16. Snyder, B.: Music and Memory. An Introduction. The MIT Press, Cambridge (2000) 17. The Digital Tradition (2010), http://www.mudcat.org/AboutDigiTrad.cfm (visited December 1, 2010) 18. Thomassen, J.M.: Melodic accent: Experiments and a tentative model. J. Acoust. Soc. Am. 71(6), 1596–1605 (1982) 19. Vos, P.G., Troost, J.M.: Ascending and Descending Melodic Intervals: Statistical Findings and Their Perceptual Relevance. Music Perception 6(4), 383–396 (1089) 20. Widmer, G.: Machine discoveries: A few simple, robust local expression principles. Journal of New Music Research 31, 37–50 (2002) 21. Zwicker, E., Fastl, H.: Psychoacoustics: facts and models, 2nd edn. Springer series in information sciences. Springer, Berlin (1999)

An Entropy Based Method for Local Time-Adaptation of the Spectrogram Marco Liuni1,2, , Axel R¨ obel2 , Marco Romito1 , and Xavier Rodet2 1

Universit´ a di Firenze, Dip. di Matematica ”U. Dini” Viale Morgagni, 67/a - 50134 Florence - ITALY 2 IRCAM - CNRS STMS, Analysis/Synthesis Team 1, Place Igor-Stravinsky - 75004 Paris - FRANCE {marco.liuni,axel.roebel,xavier.rodet}@ircam.fr [email protected] http://www.ircam.fr/anasyn.html

Abstract. We propose a method for automatic local time-adaptation of the spectrogram of audio signals: it is based on the decomposition of a signal within a Gabor multi-frame through the STFT operator. The sparsity of the analysis in every individual frame of the multi-frame is evaluated through the R´enyi entropy measures: the best local resolution is determined minimizing the entropy values. The overall spectrogram of the signal we obtain thus provides local optimal resolution adaptively evolving over time. We give examples of the performance of our algorithm with an instrumental sound and a synthetic one, showing the improvement in spectrogram displaying obtained with an automatic adaptation of the resolution. The analysis operator is invertible, thus leading to a perfect reconstruction of the original signal through the analysis coeﬃcients. Keywords: adaptive spectrogram, sound representation, sound analysis, sound synthesis, R´enyi entropy, sparsity measures, frame theory.

1

Introduction

Far from being restricted to entertainment, sound processing techniques are required in many diﬀerent domains: they ﬁnd applications in medical sciences, security instruments, communications among others. The most challenging class of signals to consider is indeed music: the completely new perspective opened by contemporary music, assigning a fundamental role to concepts as noise and timbre, gives musical potential to every sound. The standard techniques of digital analysis are based on the decomposition of the signal in a system of elementary functions, and the choice of a speciﬁc system necessarily has an inﬂuence on the result. Traditional methods based on single sets of atomic functions have important limits: a Gabor frame imposes a ﬁxed resolution over all the time-frequency plane, while a wavelet frame gives a strictly determined variation of the resolution: moreover, the user is frequently

This work is supported by grants from Region Ile-de-France.

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asked to deﬁne himself the analysis window features, which in general is not a simple task even for experienced users. This motivates the search for adaptive methods of sound analysis and synthesis, and for algorithms whose parameters are designed to change according to the analyzed signal features. Our research is focused on the development of mathematical models and tools based on the local automatic adaptation of the system of functions used for the decomposition of the signal: we are interested in a complete framework for analysis, spectral transformation and re-synthesis; thus we need to deﬁne an eﬃcient strategy to reconstruct the signal through the adapted decomposition, which must give a perfect recovery of the input if no transformation is applied. Here we propose a method for local automatic time-adaptation of the Short Time Fourier Transform window function, through a minimization of the R´enyi entropy [22] of the spectrogram; we then deﬁne a re-synthesis technique with an extension of the method proposed in [11]. Our approach can be presented schematically in three parts: 1. a model for signal analysis exploiting concepts of Harmonic Analysis, and Frame Theory in particular: it is a generally highly redundant decomposing system belonging to the class of multiple Gabor frames [8],[14]; 2. a sparsity measure deﬁned on time-frequency localized subsets of the analysis coeﬃcients, in order to determine local optimal concentration; 3. a reduced representation obtained from the original analysis using the information about optimal concentration, and a synthesis method through an expansion in the reduced system obtained. We have realized a ﬁrst implementation of this scheme in two diﬀerent versions: for both of them a sparsity measure is applied on subsets of analysis coeﬃcients covering the whole frequency dimension, thus deﬁning a time-adapted analysis of the signal. The main diﬀerence between the two concerns the ﬁrst part of the model, that is the single frames composing the multiple Gabor frame. This is a key point as the ﬁrst and third part of the scheme are strictly linked: the frame used for re-synthesis is a reduction of the original multi-frame, so the entire model depends on how the analysis multi-frame is designed. The section Frame Theory in Sound Analysis and Synthesis treats this part of our research in more details. The second point of the scheme is related to the measure applied on the coeﬃcients of the analysis within the multi-frame to determine local best resolutions. We consider measures borrowed from Information Theory and Probability Theory according to the interpretation of the analysis within a frame as a probability density [4]: our model is based on a class of entropy measures known as R´enyi entropies which extend the classical Shannon entropy. The fundamental idea is that minimizing the complexity or information over a set of time-frequency representations of the same signal is equivalent to maximizing the concentration and peakiness of the analysis, thus selecting the best resolution tradeoﬀ [1]: in the section R´enyi Entropy of Spectrograms we describe how a sparsity measure can consequently be deﬁned through an information measure. Finally,

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in the fourth section we provide a description of our algorithm and examples of adapted spectrogram for diﬀerent sounds. Some examples of this approach can be found in the literature: the idea of gathering a sparsity measure from R´enyi entropies is detailed in [1], and in [13] a local time-frequency adaptive framework is presented exploiting this concept, even if no methods for perfect reconstruction are provided. In [21] sparsity is obtained through a regression model; a recent development in this sense is contained in [14] where a class of methods for analysis adaptation are obtained separately in the time and frequency dimension together with perfect reconstruction formulas: indeed no strategies for automatization are employed, and adaptation has to be managed by the user. The model conceived in [18] belongs to this same class but presents several novelties in the construction of the Gabor multiframe and in the method for automatic local time-adaptation. In [15] another time-frequency adaptive spectrogram is deﬁned considering a sparsity measure called energy smearing, without taking into account the re-synthesis task. The concept of quilted frame, recently introduced in [9], is the ﬁrst promising eﬀort to establish a uniﬁed mathematical model for all the various frameworks cited above.

2

Frame Theory in Sound Analysis and Synthesis

When analyzing a signal through its decomposition, the features of the representation are inﬂuenced by the decomposing functions; the Frame Theory (see [3],[12] for detailed mathematical descriptions) allows a uniﬁed approach when dealing with diﬀerent bases and systems, studying the properties of the operators that they identify. The concept of frame extends the one of orthonormal basis in a Hilbert space, and it provides a theory for the discretization of time-frequency densities and operators [8], [20], [2]. Both the STFT and the Wavelet transform can be interpreted within this setting (see [16] for a comprehensive survey of theory and applications). Here we summarize the basic deﬁnitions and theorems, and outline the fundamental step consisting in the introduction of Multiple Gabor Frames, which is comprehensively treated in [8]. The problem of standard frames is that the decomposing atoms are deﬁned from the same original function, thus imposing a limit on the type of information that one can deduce from the analysis coeﬃcients; if we were able to consider frames where several families of atoms coexist, than we would have an analysis with variable information, at the price of a higher redundancy. 2.1

Basic Definitions and Results

Given a Hilbert space H seen as a vector space on C, with its own scalar product, we consider in H a set of vectors {φγ }γ∈Γ where the index set Γ may be inﬁnite and γ can also be a multi-index. The set {φγ }γ∈Γ is a frame for H if there exist two positive non zero constants A and B, called frame bounds, such that for all f ∈ H,

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|f, φγ |2 ≤ Bf 2 .

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(1)

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We are interested in the case H = L2 (R) and Γ countable, as it represents the standard situation where a signal f is decomposed through a countable set of given functions {φk }k∈Z . The frame bounds A and B are the inﬁmum and supremum, respectively, of the eigenvalues of the frame operator U, deﬁned as Uf = f, φk φk . (2) k∈Z

For any frame {φk }k∈Z there exist dual frames {φ˜k }k∈Z such that for all f ∈ L2 (R) f, φk φ˜k = f, φ˜k φk , (3) f= k∈Z

k∈Z

so that given a frame it is always possible to perfectly reconstruct a signal f using the coeﬃcients of its decomposition through the frame. The inverse of the frame operator allows the calculation of the canonical dual frame φ˜k = U−1 φk

(4)

which guarantees minimal-norm coeﬃcients in the expansion. A Gabor frame is obtained by time-shifting and frequency-transposing a window function g according to a regular grid. They are particularly interesting in the applications as the analysis coeﬃcients are simply given by sampling the STFT of f with window g according to the nodes of a speciﬁed lattice. Given a time step a and a frequency step b we write {un }n∈Z = an and {ξk }k∈Z = bk; these two sequences generate the nodes of the time-frequency lattice Λ for the frame {gn,k }(n,k)∈Z2 deﬁned as gn,k (t) = g(t − un )e2πiξk t ;

(5)

the nodes are the centers of the Heisenberg boxes associated to the windows in the frame. The lattice has to satisfy certain conditions for {gn,k } to be a frame [7], which impose limits on the choice of the time and frequency steps: for certain choices [6] which are often adopted in standard applications, the frame operator takes the form of a multiplication, |g(t − un )|2 f (t) , Uf (t) = b−1 (6) n∈Z

and the dual frame is easily calculated by means of a straight multiplication of the atoms in the original frame. The relation between the steps a, b and the frame bounds A, B in this case is clear by (6), as the frame condition implies 0 < A ≤ b−1 |g(t − un )|2 ≤ B < ∞ . (7) n∈Z

Thus we see that the frame bounds provide also information on the redundancy of the decomposition of the signal within the frame.

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Multiple Gabor Frames

In our adaptive framework, we look for a method to achieve an analysis with multiple resolutions: thus we need to combine the information coming from the decompositions of a signal in several frames of diﬀerent window functions. Multiple Gabor frames have been introduced in [22] to provide the original Gabor analysis with ﬂexible multi-resolution techniques: given a set of index L ⊆ Z and l diﬀerent frames {gn,k }(n,k)∈Z2 with l ∈ L, a multiple Gabor frame is obtained with a union of the single given frames. The diﬀerent g l do not necessarily share the same type or shape: in our method an original window is modiﬁed with a ﬁnite number of scaling 1 t g l (t) = √ g ; (8) l l then all the scaled versions are used to build |L| diﬀerent frames which constitute the initial multi-frame. A Gabor multi-frame has in general a signiﬁcant redundancy which lowers the readability of the analysis. A possible strategy to overcome this limit is proposed in [14] where nonstationary Gabor frames are introduced, actually allowing the choice of a diﬀerent window for each time location of a global irregular lattice Λ, or alternatively for each frequency location. This way, the window chosen is a function of time or frequency position in the time-frequency space, not both. In most applications, for this kind of frame there exist fast FFT based methods for the analysis and re-synthesis steps. Referring to the time case, with the abuse of notation gn(l) we indicate the window g l centered at a certain time n(l) = un which is a function of the chosen window itself. Thus, a nonstationary Gabor frame is given by the set of atoms {gn(l) e2πibl kt , (n(l), bl k) ∈ Λ} ,

(9)

where bl is the frequency step associated to the window g l and k ∈ Z . If we suppose that the windows g l have limited time support and a suﬃciently small frequency step bl , the frame operator U takes a similar form to the one in (6), Uf (t) =

1 |gn(l) (t)|2 f (t) . bl

(10)

n(l)

Here, if N = l b1l |gn(l) (s)|2 1 then U is invertible and the set (9) is a frame whose dual frame is given by g˜n(l),k (t) =

1 gn(l) (t)e2πibl kt . N

(11)

Nonstationary Gabor frames belong to the recently introduced class of quilted frames [9]: in this kind of decomposing systems the choice of the analysis window depends on both the time and the frequency location, causing more diﬃculties

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for an analytic fast computation of a dual frame as in (11): future improvements of our research concern the employment of such a decomposition model for automatic local adaptation of the spectrogram resolution both in the time and the frequency dimension.

3

R´ enyi Entropy of Spectrograms

We consider the discrete spectrogram of a signal as a sampling of the square of its continuous version 2 2 −2πiξt PSf (u, ξ) = |Sf (u, ξ)| = f (t)g(t − u)e dt , (12) where f is a signal, g is a window function and Sf (u, ξ) is the STFT of f through g. Such a sampling is obtained according to a regular lattice Λab , considering a Gabor frame (5), PSf [n, k] = |Sf [un , ξk ]|2 . (13) With an appropriate normalization both the continuous and discrete spectrogram can be interpreted as probability densities. Thanks to this interpretation, some techniques belonging to the domain of Probability and Information Theory can be applied to our problem: in particular, the concept of entropy can be extended to give a sparsity measure of a time-frequency density. A promising approach [1] takes into account R´enyi entropies, a generalization of the Shannon entropy: the application to our problem is related to the concept that minimizing the complexity or information of a set of time-frequency representations of a same signal is equivalent to maximizing the concentration, peakiness, and therefore the sparsity of the analysis. Thus we will consider as best analysis the sparsest one, according to the minimal entropy evaluation. Given a signal f and its spectrogram PSf as in (12), the R´enyi entropy of order α > 0, α = 1 of PSf is deﬁned as follows α 1 PSf (u, ξ)

HR (PS ) = log dudξ , (14) f α 2 1−α PSf (u , ξ )du dξ R R where R ⊆ R2 and we omit its indication if equality holds. Given a discrete spectrogram with time step a and frequency step b as in (13), we consider R as a rectangle of the time-frequency plane R = [t1 , t2 ] × [ν1 , ν2 ] ⊆ R2 . It identiﬁes a sequence of points G ⊆ Λab where G = {(n, k) ∈ Z2 : t1 ≤ na ≤ t2 , ν1 ≤ kb ≤ ν2 }. As a discretization of the original continuous spectrogram, every sample in PSf [n, k] is related to a time-frequency region of area ab; we thus obtain the discrete R´enyi entropy measure directly from (14), α 1 PSf [n, k] HG [PS ] = log + log2 (ab) . (15) f 2 α 1−α [n ,k ]∈G PSf [n , k ] [n,k]∈G

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We will focus on discretized spectrograms with a ﬁnite number of coeﬃcients, as dealing with digital signal processing requires to work with ﬁnite sampled signals and distributions. Among the general properties of R´enyi entropies [17], [19] and [23] we recall in particular those directly related with our problem. It is easy to show that for every ﬁnite discrete probability density P the entropy Hα (P ) tends to coincide with the Shannon entropy of P as the order α tends to one. Moreover, Hα (P ) is a non increasing function of α, so α1 < α2 ⇒ Hα1 (P ) ≥ Hα2 (P ) .

(16)

As we are working with ﬁnite discrete densities we can also consider the case α = 0 which is simply the logarithm of the number of elements in P ; as a consequence H0 (P ) ≥ Hα (P ) for every admissible order α. A third basic fact is that for every order α the R´enyi entropy Hα is maximum when P is uniformly distributed, while it is minimum and equal to zero when P has a single non-zero value. All of these results give useful information on the values of diﬀerent measures on a single density P as in (15), while the relations between the entropies of two diﬀerent densities P and Q are in general hard to determine analytically; in our problem, P and Q are two spectrograms of a signal in the same time-frequency area, based on two window functions with diﬀerent scaling as in (8). In some basic cases such a relation is achievable, as shown in the following example. 3.1

Best Window for Sinusoids

When the spectrograms of a signal through diﬀerent window functions do not depend on time, it is easy to compare their entropies: let PSs be the sampled spectrogram of a sinusoid s over a ﬁnite region G with a window function g of compact support; then PSs is simply a translation in the frequency domain of gˆ, the Fourier transform of the window, and it is therefore time-independent. We choose a bounded set L of admissible scaling factors, so that the discretized support of the scaled windows g l still remains inside G for any l ∈ L. It is not hard to prove that the entropy of a spectrogram taken with such a scaled version of g is given by G HG α (PSsl ) = Hα (PSs ) − log2 l .

(17)

The sparsity measure we are using chooses as best window the one which minimizes the entropy measure: we deduce from (17) that it is the one obtained with the largest scaling factor available, therefore with the largest time-support. This is coherent with our expectation as stationary signals, such as sinusoids, are best analyzed with a high frequency resolution, because time-independency allows a small time resolution. Moreover, this is true for any order α used for the entropy calculus. Symmetric considerations apply whenever the spectrogram of a signal does not depend on frequency, as for impulses.

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The α Parameter

The α parameter in (14) introduces a biasing on the spectral coeﬃcients; to have a qualitative description of this biasing, we consider a collection of simple spectrograms composed by a variable amount of large and small coeﬃcients. We realize a vector D of length N = 100 generating numbers between 0 and 1 with a normal random distribution; then we consider the vectors DM , 1 ≤ M ≤ N such that D[k] if k ≤ M DM [k] = D[k] (18) 20 if k > M and then normalize to obtain a unitary sum. We then apply R´enyi entropy measures with α varying between 0 and 30: as we see from ﬁgure 1, there is a relation between M and the slope of the entropy curves for the diﬀerent values of α. For α = 0, H0 [DM ] is the logarithm of the number of non-zero coeﬃcients and it is therefore constant; when α increases, we see that densities with a small amount of large coeﬃcients gradually decrease their entropy, faster than the almost ﬂat vectors corresponding to larger values of M . This means that by increasing α we emphasize the diﬀerence between the entropy values of a peaky distribution and that of a nearly ﬂat one. The sparsity measure we consider select as best analysis the one with minimal entropy, so reducing α rises the probability of less peaky distributions to be chosen as sparsest: in principle, this is desirable as weaker components of the signal, such as partials, have to be taken into account in the sparsity evaluation. But as well, this principle should be applied with care as a small coeﬃcient in a spectrogram could be determined by a partial as well as by a noise component; choosing an extremely small α, the best window chosen could vary without a reliable relation with spectral concentration depending on the noise level within the sound.

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Time and Frequency Steps

A last remark regards the dependency of (15) on the time and frequency step a and b used for the discretization of the spectrogram. When considering signals as ﬁnite vectors, a signal and its Fourier Transform have the same length. Therefore in the STFT the window length determines the number frequency points, while the sampling rate sets frequency values: the deﬁnition of b is thus implicit in the window choice. Actually, the FFT algorithm allows to specify a number of frequency points larger than the signal length: further frequency values are obtained as an interpolation between the original ones by properly adding zero values to the signal. If the sampling rate is ﬁxed, this procedure causes a smaller b as a consequence of a larger number of frequency points. We have numerically veriﬁed that such a variation of b has no impact on the entropy calculus, so that the FFT size can be set according to implementation needs. Regarding the time step a, we are working on the analytical demonstration of a largely veriﬁed evidence: as long as the decomposing system is a frame the entropy measure is invariant to redundancy variation, so the choice of a can be ruled by considerations on the invertibility of the decomposing frame without losing coherence between the information measure of the diﬀerent analyses. This is a key point, as it states that the sparsity measure obtained allows a total independence between the hop sizes of the diﬀerent analyses: with the implementation of proper structures to handle multi-hop STFTs we have obtained a more eﬃcient algorithm in comparison with those imposing a ﬁxed hop size, as [15] and the ﬁrst version of the one we have realized.

4

Algorithm and Examples

We now summarize the main operations of the algorithm we have developed providing examples of its application. For the calculation of the spectrograms we use a Hanning window h(t) = cos2 (πt)χ[− 12 , 12 ] ,

(19)

with χ the indicator function of the speciﬁed interval, but it is obviously possible to generalize the results thus obtained to the entire class of compactly supported window functions. In both the versions of our algorithm we create a multiple Gabor frame as in (5), using as mother functions some scaled version of h, obtained as in (8) with a ﬁnite set of positive real scaling factors L. We consider consecutive segments of the signal, and for each segment we calculate |L| spectrograms with the |L| scaled windows: the length of the analysis segment and the overlap between two consecutive segments are given as parameters. In the ﬁrst version of the algorithm the diﬀerent frames composing the multiframe have the same time step a and frequency step b: this guarantees that for each signal segment the diﬀerent frames have Heisenberg boxes whose centers lay on a same lattice on the time-frequency plane, as illustrated in ﬁgure 2. To

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Fig. 3. Two diﬀerent spectrograms of a B4 note played by a marimba, with Hanning windows of sizes 512 (top) and 4096 (bottom) samples

guarantee that all the |L| scaled windows constitute a frame when translated and modulated according to this global lattice, the time step a must be set with the hop size assigned to the smallest window frame. On the other hand, as the FFT of a discrete signal has the same number of points of the signal itself, the frequency step b has to be the FFT size of the largest window analysis: for the smaller ones, a zero-padding is performed.

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Fig. 4. Example of an adaptive analysis performed by the ﬁrst version of our algorithm with four Hanning windows of diﬀerent sizes (512, 1024, 2048 and 4096 samples) on a B4 note played by a marimba: on top, the best window chosen as a function of time; at the bottom, the adaptive spectrogram. The entropy order is α = 0.7 and each analysis segment contains twenty-four analyses frames with a sixteen-frames overlap between consecutive segments.

Each signal segment identiﬁes a time-frequency rectangle G for the entropy evaluation: the horizontal edge is the time interval of the considered segment, while the vertical one is the whole frequency lattice. For each spectrogram, the rectangle G deﬁnes a subset of coeﬃcients belonging to G itself. The |L| diﬀerent subsets do not correspond to the same part of signal, as windows have diﬀerent time supports. Therefore, a preliminary weighting of the signal has to be performed before the calculations of the local spectrograms: this step is necessary to balance the inﬂuence on the entropy calculus between coeﬃcients which regard parts of signal shared or not shared by the diﬀerent analysis frames. After the pre-weighting, we calculate the entropy of every spectrogram as in (15). Having the |L| entropy values corresponding to the diﬀerent local spectrograms, the sparsest local analysis is deﬁned as the one with minimum R´enyi entropy: the window associated to the sparsest local analysis is chosen as best window at all the time points contained in G. The global time adapted analysis of the signal is ﬁnally realized by opportunely assembling the slices of local sparsest analyses: they are obtained with a further spectrogram calculation of the unweighted signal, employing the best windows selected at each time point. In ﬁgure 4 we give an example of an adaptive analysis performed by our ﬁrst algorithm with four Hanning windows of diﬀerent sizes on a real instrumental sound, a B4 note played by a marimba: this sound combines the need for a

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Fig. 5. An analysis segment: time locations of the Heisenberg boxes associated to the multi-frame used in the second version of our algorithm

good time resolution at the strike with that of a good frequency resolution on the harmonic resonance. This is fully provided by the algorithm, as shown in the adaptive spectrogram at the bottom of the ﬁgure 4. Moreover, we see that the pre-echo of the analysis at the bottom of ﬁgure 3 is completely removed in the adapted spectrogram. The main diﬀerence in the second version of our algorithm concerns the individual frames composing the multi-frame, which have the same frequency step b but diﬀerent time steps {al : l ∈ L}: the smallest and largest window sizes are given as parameters together with |L|, the number of diﬀerent windows composing the multi-frame, and the global overlap needed for the analyses. The algorithm ﬁxes the intermediate sizes so that, for each signal segment, the different frames have the same overlap between consecutive windows, and so the same redundancy. This choice highly reduces the computational cost by avoiding unnecessary small hop sizes for the larger windows, and as we have observed in the previous section it does not aﬀect the entropy evaluation. Such a structure generates an irregular time disposition of the multi-frame elements in each signal segment, as illustrated in ﬁgure 5; in this way we also avoid the problem of unshared parts of signal between the systems, but we still have a diﬀerent inﬂuence of the boundary parts depending on the analysis frame: the beginning and the end of the signal segment have a higher energy when windowed in the smaller frames. This is avoided with a preliminary weighting: the beginning and the end of each signal segment are windowed respectively with the ﬁrst and second half of the largest analysis window. As for the ﬁrst implementation, the weighting does not concern the decomposition for re-synthesis purpose, but only the analyses used for entropy evaluations.

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Fig. 6. Example of an adaptive analysis performed by the second version of our algorithm with eight Hanning windows of diﬀerent sizes from 512 to 4096 samples, on a B4 note played by a marimba sampled at 44.1kHz: on top, the best window chosen as a function of time; at the bottom, the adaptive spectrogram. The entropy order is α = 0.7 and each analysis segment contains four frames of the largest window analysis with a two-frames overlap between consecutive segments.

After the pre-weighting, the algorithm follows the same steps described above: calculation of the |L| local spectrograms, evaluation of their entropy, selection of the window providing minimum entropy, computation of the adapted spectrogram with the best window at each time point, thus creating an analysis with time-varying resolution and hop size. In ﬁgure 6 we give a ﬁrst example of an adaptive analysis performed by the second version of our algorithm with eight Hanning windows of diﬀerent sizes: the sound is still the B4 note of a marimba, and we can see that the two versions give very similar results. Thus, if the considered application does not speciﬁcally ask for a ﬁxed hop size of the overall analysis, the second version is preferable as it highly reduces the computational cost without aﬀecting the best window choice. In ﬁgure 8 we give a second example with a synthetic sound, a sinusoid with sinusoidal frequency modulation: as ﬁgure 7 shows, a small window is best adapted where the frequency variation is fast compared to the window length; on the other hand, the largest window is better where the signal is almost stationary. 4.1

Re-synthesis Method

The re-synthesis method introduced in [11] gives a perfect reconstruction of the signal as a weighted expansion of the coeﬃcients of its STFT in the original analysis frame. Let Sf [n, k] be the STFT of a signal f with window function h and time step a; ﬁxing n, through an iFFT we have a windowed segment of f

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fh (n, l) = h(na − l)f (l) ,

(20)

whose time location depends on n. An immediate perfect reconstruction of f is given by +∞ h(na − l)fh (n, l) f (l) = n=−∞ . (21) +∞ 2 n=−∞ h (na − l) In our case, after the automatic selection step we dispose of a temporal sequence with the best windows at each time position; in the ﬁrst version we have a ﬁxed hop for all the windows, in the second one every window has its own time step. In both the cases we have thus reduced the initial multi-frame to a nonstationary Gabor frame: we extend the same technique of (21) using a variable window h and time step a according to the composition of the reduced multi-frame, obtaining a perfect reconstruction as well. The interest of (21) is that the given distribution does not need to be the STFT of a signal: for example, a transformation S ∗ [n, k] of the STFT of a signal could be considered. In this case, (21) gives the signal whose STFT has minimal least squares error with S ∗ [n, k]. As seen by the equations (9) and (11), the theoretical existence and the mathematical deﬁnition of the canonical dual frame for a nonstationary Gabor frame like the one we employ has been provided [14]: it is thus possible to deﬁne the whole analysis and re-synthesis framework within the Gabor theory. We are at present working on the interesting analogies between the two approaches, to establish a uniﬁed interpretation and develop further extensions.

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Fig. 8. Example of an adaptive analysis performed by the second version of our algorithm with eight Hanning windows of diﬀerent sizes from 512 to 4096 samples, on a sinusoid with sinusoidal frequency modulation synthesized at 44.1 kHz: on top, the best window chosen as a function of time; at the bottom, the adaptive spectrogram. The entropy order is α = 0.7 and each analysis segment contains four frames of the largest window analysis with a three-frames overlap between consecutive segments.

5

Conclusions

We have presented an algorithm for time-adaptation of the spectrogram resolution, which can be easily integrated in existent framework for analysis, transformation and re-synthesis of an audio signal: the adaptation is locally obtained through an entropy minimization within a ﬁnite set of resolutions, which can be deﬁned by the user or left as default. The user can also specify the time duration and overlap of the analysis segments where entropy minimization is performed, to privilege more or less discontinuous adapted analyses. Future improvements of this method will concern the spectrogram adaptation in both time and frequency dimensions: this will provide a decomposition of the signal in several layers of analysis frames, thus requiring an extension of the proposed technique for re-synthesis.

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3. Christensen, O.: An Introduction To Frames And Riesz Bases. Birkh¨ auser, Boston (2003) 4. Cohen, L.: Time-Frequency Distributions-A Review. Proceedings of the IEEE 77(7) (1989) 5. Cohen, L.: Time-Frequency Analysis. Prentice-Hall, Upper Saddle River (1995) 6. Daubechies, I., Grossmann, A., Meyer, Y.: Painless nonorthogonal expansions. J. Math. Phys. 27 (1986) 7. Daubechies, I.: The Wavelet Transform, Time-Frequency Localization and Signal Analysis. IEEE Trans. Info. Theory 36(5) (1990) 8. D¨ orﬂer, M.: Gabor Analysis for a Class of Signals called Music. PhD thesis, NuHAG, University of Vienna (2002) 9. D¨ orﬂer, M.: Quilted Gabor frames - a new concept for adaptive time-frequency representation. eprint arXiv:0912.2363 (2010) 10. Flandrin, P.: Time-Frequency/ Time-Scale Analysis. Academic Press, San Diego (1999) 11. Griﬃn, D.W., Lim, J.S.: Signal Estimation from Modiﬁed Short-Time Fourier Transform. IEEE Trans. Acoust. Speech Signal Process. 32(2) (1984) 12. Gr¨ ochenig, K.: Foundations of Time-Frequency Analysis. Birkh¨ auser, Boston (2001) 13. Jaillet, F.: Repr´esentation et traitement temps-fr´equence des signaux audionum´eriques pour des applications de design sonore. PhD thesis, Universit´e de la M´editerran´ee - Aix-Marseille II (2005) 14. Jaillet, F., Balazs, P., D¨ orﬂer, M.: Nonstationary Gabor Frames. INRIA a CCSD electronic archive server based on P.A.O.L (2009), http://hal.inria.fr/oai/oai.php 15. Lukin, A., Todd, J.: Adaptive Time-Frequency Resolution for Analysis and Processing of Audio. Audio Engineering Society Convention Paper (2006) 16. Mallat, S.: A wavelet tour on signal processing. Academic Press, San Diego (1999) 17. R´enyi, A.: On Measures of Entropy and Information. In: Proc. Fourth Berkeley Symp. on Math. Statist. and Prob., Berkeley, California, pp. 547–561 (1961) 18. Rudoy, D., Prabahan, B., Wolfe, P.: Superposition frames for adaptive timefrequency analysis and fast reconstruction. IEEE Trans. Sig. Proc. 58(5) (2010) 19. Schl¨ ogl, F., Beck, C. (eds.): Thermodynamics of chaotic systems. Cambridge University Press, Cambridge (1993) 20. Sun, W.: Asymptotic properties of Gabor frame operators as sampling density tends to inﬁnity. J. Funct. Anal. 258(3) (2010) 21. Wolfe, P.J., Godsill, S.J., D¨ orﬂer, M.: Multi-Gabor Dictionaries for Audio TimeFrequency Analysis. In: Proc. IEEE WASPAA (2001) 22. Zibulski, M., Zeevi, Y.Y.: Analysis of multiwindow Gabor-type schemes by frame methods. Appl. Comput. Harmon. Anal. 4(2) (1997) 23. Zyczkowski, K.: R´enyi Extrapolation of Shannon Entropy. Open Systems & Information Dynamics 10(3) (2003)

Transcription of Musical Audio Using Poisson Point Processes and Sequential MCMC Pete Bunch and Simon Godsill Signal Processing and Communications Laboratory Department of Engineering University of Cambridge {pb404,sjg}@eng.cam.ac.uk http://www-sigproc.eng.cam.ac.uk/~ sjg

Abstract. In this paper models and algorithms are presented for transcription of pitch and timings in polyphonic music extracts. The data are decomposed framewise into the frequency domain, where a Poisson point process model is used to write a polyphonic pitch likelihood function. From here Bayesian priors are incorporated both over time (to link successive frames) and also within frames (to model the number of notes present, their pitches, the number of harmonics for each note, and inharmonicity parameters for each note). Inference in the model is carried out via Bayesian filtering using a powerful Sequential Markov chain Monte Carlo (MCMC) algorithm that is an MCMC extension of particle filtering. Initial results with guitar music, both laboratory test data and commercial extracts, show promising levels of performance. Keywords: Automated music transcription, multi-pitch estimation, Bayesian filtering, Poisson point process, Markov chain Monte Carlo, particle filter, spatio-temporal dynamical model.

1

Introduction

The audio signal generated by a musical instrument as it plays a note is complex, containing multiple frequencies, each with a time-varying amplitude and phase. However, the human brain perceives such a signal as a single note, with associated “high-level” properties such as timbre (the musical “texture”) and expression (loud, soft, etc.). A musician playing a piece of music takes as input a score, which describes the music in terms of these high-level properties, and produces a corresponding audio signal. An accomplished musician is also able to reverse the process, listening to a musical audio signal and transcribing a score. A desirable goal is to automate this transcription process. Further developments in computer “understanding” of audio signals of this type can be of assistance to musicologists; they can also play an important part in source separation systems, as well as in automated mark-up systems for content-based annotation of music databases. Perhaps the most important property to extract in the task of musical transcription is the note or notes playing at each instant. This will be the primary S. Ystad et al. (Eds.): CMMR 2010, LNCS 6684, pp. 76–83, 2011. c Springer-Verlag Berlin Heidelberg 2011

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aim of this work. As a subsidiary objective, it can be desirable to infer other high level properties, such as timbre, expression and tempo. Music transcription has become a large and active ﬁeld over recent years, see e.g. [6], and recently probabilistic Bayesian approaches have attracted attention, see e.g. [5,2,1] to list but a few of the many recent contributions to this important area. The method considered in this paper is an enhanced form of a frequency domain model using a Poisson point process ﬁrst developed in musical modelling applications in [8,1]. The steps of the process are as follows. The audio signal is ﬁrst divided into frames, and an over-sampled Fast Fourier Transform (FFT) is performed on each frame to generate a frequency spectrum. The predominant peaks are then extracted from the amplitude of the frequency data. A likelihood function for the observed spectral peaks may then be formulated according to an inhomogeneous Poisson point process model (see [8] for the static single frame formulation), conditional on all of the unknown parameters (the number of notes present, their pitches, the number of harmonics for each note, and inharmonicity parameters for each note). A prior distribution for these parameters, including their evolution over time frames, then completes a Bayesian spatio-temporal state space model. Inference in this model is carried out using a specially modiﬁed version of the sequential MCMC algorithm [7], in which information about the previous frame is collapsed onto a single univariate marginal representation of the multipitch estimation. To summarise the new contributions of this paper, we here explicitly model within the Poisson process framework the number of notes present, the number of harmonics for each note and the inharmonicity parameter for each note, and we model the temporal evolution of the notes over time frames, all within a fully Bayesian sequential updating scheme, implemented with sequential MCMC. This contrasts with, for example, the static single frame-based approach of our earlier Poisson process transcription work [8].

2 2.1

Models and Algorithms The Poisson Likelihood Model

When a note is played on a musical instrument, a vibration occurs at a unique “fundamental frequency”. In addition, an array of “partial frequencies” is also generated. To a ﬁrst order approximation, these occur at integer multiples of the fundamental frequency. In fact, a degree of inharmonicity will usually occur, especially for plucked or struck string instruments [4] (including the guitars considered as examples in this work). The inclusion of inharmonicity in the Poisson likelihood models here adopted has not been considered before to our knowledge. In this paper, compared with [8], we introduce an additional inharmonicity parameter for each musical pitch. This is incorporated in a similar fashion to the inharmonicity model in [1], in which an entirely diﬀerent time domain signal model was adopted. We consider ﬁrstly a single frame of data, as in [8], then extend to the sequential modelling of many frames. Examining the spectrum of a single note,

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Fig. 1. An example of a single note spectrum, with the associated median threshold (using a window of ±4 frequency bins) and peaks identified by the peak detection algorithm (circles)

such as that in Figure 1, it is evident that a substantial part of the information about pitch is contained in the frequency and amplitude of the spectral peaks. The amplitudes of these peaks will vary according to the volume of the note, the timbre of the instrument, and with time (high frequency partials will decay faster, interference will cause beating, etc.), and are thus challenging to model. Here, then, for reasons of simplicity and robustness of the model, we consider only the frequencies at which peaks are observed. The set of observed peaks is constructed by locating frequencies which have an amplitude larger than those on each side, and which also exceeds a median ﬁlter threshold. See Figure 1. for an illustration of the procedure. For the Poisson likelihood model, the occurrence of peaks in the frequency domain is assumed to follow an inhomogeneous Poisson process, in which an intensity function μk gives the mean value of a Poisson distribution at the kth frequency bin (μk is the integral, over the kth frequency bin width, of an intensity function deﬁned in continuous frequency, μ(f )). The principal advantage of such a model is that we do not have to perform data association: there is no need to identify uniquely which spectral peak belongs to which note harmonic. A consequence of this simpliﬁcation is that each harmonic in each musical note is deemed capable of generating more than one peak in the spectrum. Examining the k th FFT frequency bin, with Poisson intensity μk and in which Zk peaks occur, the probability of observing n spectral peaks is given by the Poisson distribution: e−µk μnk P (Zk = n) = (1) n! In fact, since it is not feasible to observe more than a single peak in each frequency bin, we here consider only the binary detection of either zero peaks, or ‘one or more’ peaks, as in [8]:

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Zero Peaks: P (Zk = 0) = e−µk

One or more peaks: P (Zk ≥ 1) = 1 − e−µk (2) A single frame of data can thus be expressed as a binary vector where each term inicates the presence or absence of a peak in the corresponding frequency bin. As the bin observations are independent (following from the the Poisson process assumption), the likelihood of the observed spectrum is given by: P (Y |μ) =

K

[yk (1 − e−µk ) + (1 − yk )e−µk ]

(3)

k=1

where Y = {y1 , y2 , ..., yK } are the observed peak data in the K frequency bins, such that yk = 1 if a peak is observed in the k th bin, and yk = 0 otherwise. It only remains to formulate the intensity function μ(f ), and hence μk = f ∈ kth bin μ(f )df . For this purpose, the Gaussian mixture model of Peeling et al.[8] is used. Note that in this formulation we can regard each harmonic of each note to be an independent Poisson process itself, and hence by the union property of Poisson processes, all of the individual Poisson intensities add to give a single overall intensity μ, as follows: μ(f ) =

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(5)

where j indicates the note number, h indicates the partial number, and N and Hj are the numbers of notes and harmonics in each note, respectively. μc is a constant that accounts for detected “clutter” peaks due to noise and non-musical 2 sounds. σj,h = κ2 h2 sets the variance of each Gaussian. A and κ are constant parameters, chosen so as to give good performance on a set of test pieces. fj,h is the frequency of the hth partial of the j th note, given by the inharmonic model [4]: fj,h = f0,j h 1 + Bj h2

(6)

f0,j is the fundamental frequency of the jth note. Bj is the inharmonicity parameter for the note (of the order 10−4 ). Three parameters for each note are variable and to be determined by the inference engine: the fundamental, the number of partials,and the inharmonicity. Moreover, the number of notes N is also treated as unknown in the fully Bayesian framework. 2.2

Prior Distributions and Sequential MCMC Inference

The prior P (θ) over the unknown parameters θ in each time frame may be decomposed, assuming parameters of diﬀerent notes are independent, as:

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P (θ) = P (N ) ×

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(7)

j=1

In fact, we have here assumed all priors to be uniform over their expected ranges, except for f0,j and N , which are stochastically linked to their values in previous frames. To consider this linkage explicitly, we now introduce a frame number label t and the corresponding parameters for frame t as θ t , with frame peak data Y t . In order to carry out optimal sequential updating we require a transition density p(θ t |θt−1 ), and assume that the {θt } process is Markovian. Then we can write the required sequential update as: p(θ t−1:t |Y1:t ) ∝ p(θ t−1 |Y1:t−1 )p(θ t |θt−1 )p(Yt |θ t )

(8)

To see how this can be implemented in a sequential MCMC framework, assume that at time t − 1 the inference problem is solved and a set of M >> 1 Monte (i) Carlo (dependent) samples {θt−1 } are available from the previous time’s target distribution p(θ t−1 |Y1:t−1 ). These samples are then formed into an empirical distribution pˆ(θt−1 ) which is used as an approximation to p(θ t−1 |Y1:t−1 ) in Eq. (8). This enables the (approximated) time updated distribution p(θ t−1:t |Y1:t ) to be evaluated pointwise, and hence a new MCMC chain can be run with Eq. (8) as its target distribution. The converged samples from this chain are then used to approximate the posterior distribution at time t, and the whole procedure repeats as time step t increases. The implementation of the MCMC at each time step is quite complex, since it will involve updating all elements of the parameter vector θ t , including the number of notes, the fundamental frequencies, the number of harmonics in each note and the inharmonicity parameter for each note. This is carried out via a combination of Gibbs sampling and Metropolis-within-Gibbs sampling, using a Reversible Jump formulation wherever the parameter dimension (i.e. the number of notes in the frame) needs to change, see [7] for further details of how such schemes can be implemented in tracking and ﬁnance applications and [3] for general information about MCMC. In order to enhance the practical performance we modiﬁed the approximating density at t − 1, pˆ(θ t−1 ) to be a univariate density over one single fundamental frequency, which can be thought of as the posterior distribution of fundamental frequency at time t − 1 with all the other parameters marginalised, including the number of notes, and a univariate density over the number of notes. This collapsing of the posterior distribution onto a univariate marginal, although introducing an additional approximation into the updating formula, was found to enhance the MCMC exploration at the next time step signiﬁcantly, since it avoids combinatorial updating issues that increase dramatically with the dimension of the full parameter vector θ t . Having carried out the MCMC sampling at each time step, the fundamental frequencies and their associated parameters (inharmonicity and number of harmonics, if required) may be estimated. This estimation is based on extracting maxima from the collapsed univariate distribution over fundamental frequency, as described in the previous paragraph.

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(b) 3-note chords

(c) Tears in Heaven Fig. 2. Reversible Jump MCMC Results: Dots indicate note estimates. Line below indicates estimate of the number of notes. Crosses in panels (a) and (b) indicate notes estimated by the MCMC algorithm but removed by post-processing. A manually obtained ground-truth is shown overlayed in panel (c).

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Results

The methods have been evaluated on a selection of guitar music extracts, recorded both in the laboratory and taken from commercial recordings. See Fig. 2 in which three guitar extracts, two lab-generated (a) and (b) and one from a commercial recording (c) are processed. Note that a few spurious note estimates arise, particularly around instants of note change, and many of these have been removed by a post-processing stage which simply eliminates note estimates which last for a single frame. The results are quite accurate, agreeing well with manually obtained transcriptions. When two notes an octave apart are played together, the upper note is not found. See ﬁnal chord of panel (a) in Figure 2. This is attributable to the two notes sharing many of the same partials, making discrimination diﬃcult based on peak frequencies alone. In the case of strong notes, the algorithm often correctly identiﬁes up to 35 partial frequencies. In this regard, the use of inharmonicity modelling has proved succesful: Without this feature, the estimate of the number of harmonics is often lower, due to the inaccurate partial frequencies predicted by the linear model. The eﬀect of the sequential formulation is to provide a degree of smoothing when compared to the frame-wise algorithm. Fewer single-frame spurious notes appear, although these are not entirely removed, as shown in Figure 2. Octave errors towards the end of notes are also reduced.

4

Conclusions and Future Work

The new algorithms have shown signiﬁcant promise, especially given that the likelihood function takes account only of peak frequencies and not amplitudes or other information that may be useful for a transcription system. The good performance so far obtained is a result of several novel modelling and algorithmic features, notably the formulation of a ﬂexible frame-based model that can account robustly for inharmonicities, unknown numbers of notes and unknown numbers of harmonics in each note. A further key feature is the ability to link frames together via a probabilistic model; this makes the algorithm more robust in estimation of continuous fundamental frequency tracks from the data. A ﬁnal important component is the implementation through sequential MCMC, which allows us to obtain reasonably accurate inferences from the models as posed. The models may be improved in several ways, and work is underway to address these issues. A major point is that the current Poisson model accounts only for the frequencies of the peaks present. It is likely that performance may be improved by including the peak amplitudes in the model. For example, this might make it possible to distinguish more robustly when two notes an octave apart are being played. Improvements are also envisaged in the dynamical prior linking one frame to the next, which is currently quite crudely formulated. Thus, further improvements will be possible if the dependency between frames is more carefully considered, incorporating melodic and harmonic principles to generate

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likely note and chord transitions over time. Ideally also, the algorithm should be able to run in real time, processing a piece of music as it is played. Currently, however, the Matlab-based processing is at many times real time and we will study the parallel processing possibilities (as a simple starting point, the MCMC runs can be split into several shorter parallel chains at each time frame within a parallel architecture).

References 1. Cemgil, A., Godsill, S.J., Peeling, P., Whiteley, N.: Bayesian statistical methods for audio and music processing. In: O’Hagan, A., West, M. (eds.) Handbook of Applied Bayesian Analysis, OUP (2010) 2. Davy, M., Godsill, S., Idier, J.: Bayesian analysis of polyphonic western tonal music. Journal of the Acoustical Society of America 119(4) (April 2006) 3. Gilks, W.R., Richardson, S., Spiegelhalter, D.J. (eds.): Markov Chain Monte Carlo in Practice. Chapman and Hall, Boca Raton (1996) 4. Godsill, S.J., Davy, M.: Bayesian computational models for inharmonicity in musical instruments. In: Proc. of IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, New Paltz, NY (October 2005) 5. Kashino, K., Nakadai, K., Kinoshita, T., Tanaka, H.: Application of the Bayesian probability network to music scene analysis. In: Rosenthal, D.F., Okuno, H. (eds.) Computational Audio Scene Analysis, pp. 115–137. Lawrence Erlbaum Associates, Mahwah (1998) 6. Klapuri, A., Davy, M.: Signal processing methods for music transcription. Springer, Heidelberg (2006) 7. Pang, S.K., Godsill1, S.J., Li, J., Septier, F.: Sequential inference for dynamically evolving groups of objects. To appear: Barber, Cemgil, Chiappa (eds.) Inference and Learning in Dynamic Models, CUP (2009) 8. Peeling, P.H., Li, C., Godsill, S.J.: Poisson point process modeling for polyphonic music transcription. Journal of the Acoustical Society of America Express Letters 121(4), EL168–EL175 (2007)

Single Channel Music Sound Separation Based on Spectrogram Decomposition and Note Classification Wenwu Wang and Haﬁz Mustafa Centre for Vision, Speech and Signal Processing (CVSSP) University of Surrey, GU2 7XH, UK {w.wang,hm00045}@surrey.ac.uk http://www.surrey.ac.uk/cvssp

Abstract. Separating multiple music sources from a single channel mixture is a challenging problem. We present a new approach to this problem based on non-negative matrix factorization (NMF) and note classiﬁcation, assuming that the instruments used to play the sound signals are known a priori. The spectrogram of the mixture signal is ﬁrst decomposed into building components (musical notes) using an NMF algorithm. The Mel frequency cepstrum coeﬃcients (MFCCs) of both the decomposed components and the signals in the training dataset are extracted. The mean squared errors (MSEs) between the MFCC feature space of the decomposed music component and those of the training signals are used as the similarity measures for the decomposed music notes. The notes are then labelled to the corresponding type of instruments by the K nearest neighbors (K-NN) classiﬁcation algorithm based on the MSEs. Finally, the source signals are reconstructed from the classiﬁed notes and the weighting matrices obtained from the NMF algorithm. Simulations are provided to show the performance of the proposed system. Keywords: Non-negative matrix factorization, single-channel sound separation, Mel frequency cepstrum coeﬃcients, instrument classiﬁcation, K nearest neighbors, unsupervised learning.

1

Introduction

Recovering multiple unknown sources from a one-microphone signal, which is an observed mixture of these sources, is referred to as the problem of singlechannel (or monaural) sound source separation. The single-channel problem is an extreme case of under-determined separation problems, which are inherently ill-posed, i.e., more unknown variables than the number of equations. To solve the problem, additional assumptions (or constraints) about the sources or the propagating channels are necessary. For an underdetermined system with two

The work of W. Wang was supported in part by an Academic Fellowship of the RCUK/EPSRC (Grant number: EP/C509307/1).

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microphone recordings, it is possible to separate the sources based on spatial diversity using determined independent component analysis (ICA) algorithms and an iterative procedure [17]. However, unlike the techniques in e.g. ADRess [2] and DUET [18] that require at least two mixtures, the cues resulting from the sensor diversity are not available in the single channel case, and thus separation is diﬃcult to achieve based on ICA algorithms. Due to the demand from several applications such as audio coding, music information retrieval, music editing and digital library, this problem has attracted increasing research interest in recent years [14]. A number of methods have been proposed to tackle this problem. According to the recent review by Li et al. [14], these methods can be approximately divided into three categories: (1) signal modelling based on traditional signal processing techniques, such as sinusoidal modelling of the sources, e.g. [6], [23], [24]; (2) learning techniques based on statistical tools, such as independent subspace analysis [4] and non-negative matrix (or tensor) factorization, e.g. [19], [20], [27], [28], [25], [8], [30]; (3) psychoacoustical mechanism of human auditory perception, such as computational auditory scene analysis (CASA), e.g. [15], [3], [26], [32], [14]. Sinusoidal modelling methods try to decompose the signal into a combination of sinusoids, and then estimate their parameters (frequencies, amplitudes, and phases) from the mixture. These methods have been used particularly for harmonic sounds. The learning based techniques do not exploit explicitly the harmonic structure of the signals, instead they use the statistical information that is estimated from the data, such as the independence or sparsity of the separated components. The CASA based techniques build separation systems on the basis of the perceptual theory by exploiting the psychoacoustical cues that can be computed from the mixture, such as common amplitude modulation. In this paper, a new algorithm is proposed for the problem of single-channel music source separation. The algorithm is based mainly on the combination of note decomposition with note classiﬁcation. The note decomposition is achieved by a non-negative matrix factorization (NMF) algorithm. NMF has been previously used for music sound separation and transcription, see e.g. [11], [1], [7], [20], [29], [30]. In this work, we ﬁrst use the NMF algorithm in [25] to decompose the spectrogram of the music mixture into building components (musical notes). Then, Mel Frequency Cepstrum Coeﬃcients (MFCCs) feature vectors are extracted from the segmented frames of each decomposed note. To divide the separated notes into their corresponding instrument categories, the K nearest neighbor (NN) classiﬁer [10] is used. The K-NN classiﬁer is an algorithm that is simple to implement and also provides good classiﬁcation performance. The source signals are reconstructed by combining the notes having same class labels. The remainder of the paper is organized as follows. The proposed separation system is described in Section 2 in detail. Some preliminary experimental results are shown in Section 3. Discussions about the proposed method are given in Section 4. Finally, Section 5 summarises the paper.

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The Proposed Separation System

This section describes the details of the processes in our proposed sound source separation system. First, the single-channel mixture of music sources is decomposed into basic building blocks (musical notes) by applying the NMF algorithm. The NMF algorithm describes the mixture in the form of basis functions and their corresponding weights (coeﬃcients) which represent the strength of each basis function in the mixture. The next step is to extract the feature vectors of the musical notes and then classify the notes into diﬀerent source streams. Finally, the source signals are reconstructed by combining the notes with the same class labels. In this work, we assume that the instruments used to generate the music sources are known a priori. In particular, two kinds of instruments, i.e. piano and violin, were used in our study. The block diagram of our proposed system is depicted in Figure 1. 2.1

Music Decomposition by NMF

In many data analysis tasks, it is a fundamental problem to ﬁnd a suitable representation of the data so that the underlying hidden structure of the data may be revealed or displayed explicitly. NMF is a data-adaptive linear representation technique for 2-D matrices that was shown to have such potentials. Given a non-negative data matrix X, the objective of NMF is to ﬁnd two non-negative matrices W and H [12], such that X = WH

(1)

In this work, X is an S × T matrix representing the mixture signal, W is the basis matrix of dimension S × R, and H is the weighting coeﬃcient matrix of

Fig. 1. Block diagram of the proposed system

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dimension R × T . The number of bases used to represent the original matrix is described by R, i.e. the decomposition rank. Due to non-negativity constraints, this representation is purely additive. Many algorithms can be used to ﬁnd the suitable pair of W and H such that the error of the approximation is minimised, see e.g. [12], [13], [7], [20] and [30]. In this work, we use the algorithm proposed in [25] for the note decomposition. In comparison to the classical algorithm in [12], this algorithm considers additional constraints from the structure of the signal. Due to the non-negativity constraints, the time-domain signal (with negative values) needs to be transformed into another domain so that only non-negative values are present in X for an NMF algorithm to be applied. In this work, the music sound is transformed into the frequency domain using, e.g. the short-time Fourier transform (STFT). The matrix X is generated as the spectrogram of the signal, and in our study, the frame size of each segment equals to 40 ms, and 50 percent overlaps between the adjacent frames are used. An example of matrix X generated from music signals is shown in Figure 2, where two music sources with each having a music note repeating twice were mixed together. One of the sources contains musical note G4, and the other is composed of note A3. The idea of decomposing the mixture signal into individual music components is based on the observation that a music signal may be represented by a set of basic building blocks such as musical notes or other general harmonic structures. The basic building blocks are also known as basis vectors and the decomposition of the single-channel mixture into basis vectors is the ﬁrst step towards the separation of multiple source signals from the single-channel mixture. If diﬀerent sources in the mixture represent diﬀerent basis vectors, then the separation problem can be regarded as a problem of classiﬁcation of basis vectors into diﬀerent categories. The source signals can be obtained by combining the basis vectors in each category. The above mixture (or NMF) model can be equally written as X=

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W. Wang and H. Mustafa

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where wr is the rth column of W = [w1 , w2 , . . . , wR ] which contains the basis vectors, and hr is the rth row of H = [h1 , h2 , . . . , hR ]T which contains the weights or coeﬃcients of each basis function in matrix W, where the superscript T is a matrix transpose. Many algorithms including those mentioned above can be applied to obtain such basis functions and weighting coeﬃcients. For example, using the algorithm developed in [30], we can decompose the mixture in Figure 2, and the resulting basis vectors (i.e. the decomposed notes) are shown in Figure 3. From this ﬁgure, it can be observed that both note G4 and A3 are successfully separated from the mixture. As a prior knowledge, given the mixture of musical sounds containing two sources (e.g. piano and violin), two diﬀerent types of basis functions are learnt from the decomposition by the NMF algorithm. The magnitude spectrograms of the basis components (notes) of the two diﬀerent sources in the mixture are obtained by multiplying the columns of the basis matrix W to the corresponding rows of the weight matrix H. The columns of matrix W contain the information of musical notes in the mixture and corresponding rows of matrix H describe the strength of these notes. Some rows in H do not contain useful information and are therefore considered as noise. The noise components are considered separately in the classiﬁcation process to improve the quality of the separated sources. 2.2

Feature Extraction

Feature extraction is a special form of dimensionality reduction by transforming the high dimensional data into a lower dimensional feature space. It is used in both the training and classiﬁcation processes in our proposed system. The audio features that we used in this work are the MFCCs. The MFCCs are extracted on a frame-by-frame basis. In the training process, the MFCCs are extracted from a training database, and the feature vectors are then formed from these coeﬃcients. In the classiﬁcation stage, the MFCCs are extracted similarly from

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the decomposed notes obtained by the NMF algorithm. In our experiments, the frame size of 40 ms is used, which equals to 1764 samples when the sampling frequency is 44100 Hz. Examples of such feature vectors are shown in Figure 4, where the four audio ﬁles (“Piano.ﬀ.A0.wav”, “Piano.ﬀ.B0.wav”, “Violin.pizz.mf.sulG.C4B4.wav”, and “Violin.pizz.pp.sulG.C4B4.wav”) were chosen from the The University of Iowa Musical Instrument Samples Database [21] and the feature vectors are 13-dimensional. Diﬀerent dimensional features have also been examined in this work. Figure 5 and 6 show the 20-dimensional and 7dimensional MFCC feature vectors computed from the same audio frames and from the same audio signals as those in Figure 4. In comparison to Figure 4, it can be observed that the feature vectors in Figure 5 and 6 have similar shapes, even though that the higher dimensional feature vectors show more details about the signal. However, it inevitably incurs a higher computational cost if the feature dimension is increased. In our study, we choose to compute a 13 dimensional MFCCs vector for each frame in the experiments, which oﬀers a good trade-oﬀ between the classiﬁcation performance and the computational eﬃciency. 2.3

Classification of Musical Notes

The main objective of classiﬁcation is to maximally extract patterns on the basis of some conditions and is to separate one class from another. The K-NN classiﬁer, which uses a classiﬁcation rule without having the knowledge of the distribution of measurements in diﬀerent classes, is used in this paper for the separation

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Table 1. The musical note classiﬁcation algorithm 1) Calculate the 13-D MFCCs feature vectors of all the musical examples in the training database with class labels. This creates a feature space for the training data. 2) Extract similarly the MFCCs feature vectors of all separated components whose class labels need to be determined. 3) Assign the labels to all the feature vectors in the separated components to the appropriate classes via the K-NN algorithm. 4) The majority vote of feature vectors determines the class label of the separated components. 5) Optimize the classiﬁcation results by diﬀerent choices of K.

of piano and violin notes. The basic steps in music note classiﬁcation include preprocessing, feature extraction or selection, classiﬁer design and optimization. The main steps used in our system are detailed in Table 1. The main disadvantage of the classiﬁcation technique based on simple “majority voting” is that the classes with more frequent examples tend to come up in the K-nearest neighbors when the neighbors are computed from a large number of training examples [5]. Therefore, the class with more frequent training examples tends to dominate the prediction of the new vector. One possible technique to solve this problem is to weight the classiﬁcation based on the distance from the test pattern to all of its K nearest neighbors. 2.4

K-NN Classifier

This section brieﬂy describes the K-NN classiﬁer used in our algorithm. K-NN is a simple technique for pattern classiﬁcation and is particularly important for non-parametric distributions. The K-NN classiﬁer labels an unknown pattern x by the majority vote of its K-nearest neighbors [5], [9]. The K-NN classiﬁer belongs to a class of techniques based on non-parametric probability density estimation. Suppose, there is a need to estimate the density function P (x) from a given dataset. In our case, each signal in the dataset is segmented to 999 frames, and a feature vector of 13 MFCC coeﬃcients is computed for each frame. Therefore, the total number of examples in the training dataset is 52947. Similarly, an unknown pattern x is also a 13 dimensional MFCCs feature vector whose label needs to be determined based on the majority vote of the nearest neighbors. The volume V around an unknown pattern x is selected such that the number of nearest neighbors (training examples) within V are 30. We are dealing with the two-class problem with prior probability P (ωi ). The measurement distribution of the patterns in class ωi is denoted by P (x | ωi ). The measurement of posteriori class probability P (ωi | x) decides the label of an unknown feature vector of the separated note. The approximation of P (x) is given by the relation [5], [10] K P (x) (3) NV

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where N is the total number of examples in the dataset, V is the volume surrounding unknown pattern x and K is the number of examples within V . The class prior probability depends on the number of examples in the dataset P (ωi ) =

Ni N

(4)

and the mesurement distribution of patterns in class ωi is deﬁned as P (x | ωi ) =

Ki Ni V

(5)

According to the Bayes theorem, the posteriori probability becomes P (ωi | x) =

P (x | ωi )P (ωi ) P (x)

(6)

Based on the above equations, we have [10] P (ωi | x) =

Ki K

(7)

i The discriminant function gi (x) = K K assigns the class label to an unknown pattern x based on the majority of examples Ki of class ωi in volume V .

2.5

Parameter Selection

The most important parameter in the K-NN algorithm is the user-deﬁned constant K. The best value of K depends upon the given data for classiﬁcation [5]. In general, the eﬀect of noise on classiﬁcation may be reduced by selecting a higher value of K. The problem arises when a large value of K is used for less distinct boundaries between classes [31]. To select good value of K, many heuristic techniques such as cross-validation may be used. In the presence of noisy or irrelevant features the performance of K-NN classiﬁer may degrade severely [5]. The selection of feature scales according to their importance is another important issue. For the improvement of classiﬁcation results, a lot of eﬀort has been devoted to the selection or scaling of the features in a best possible way. The optimal classiﬁcation results are achieved for most datasets by selecting K = 10 or more. 2.6

Data Preparation

For the classiﬁcation of separated components from mixture, the features i.e. the MFCCs, are extracted from all the signals in the training dataset and put the label on all feature vectors according to their classes (piano or violin). The labels of the feature vectors of the separated components are not known which need to be classiﬁed. Each feature vector consist of 13 MFCCs. When computing the MFCCs, the training signals and the separated components are all divided into frames with each having a length of 40 ms and 50 percent overlap between

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the frames is used to avoid discontinuities between the neighboring frames. The similarity measure of the feature vectors of the separated components to the feature vectors obtained from the training process determines which class the separated notes belong to. This is achieved by the K-NN classiﬁer. If majority vote goes to the piano, then a piano label is assigned to the separated component and vice-versa. 2.7

Phase Generation and Source Reconstruction

The factorization of magnitude spectrogram by the NMF algorithm provides frequency-domain basis functions. Therefore, the reconstruction of source signals from the frequency-domain bases is used in this paper, where the phase information is required. Several phase generation methods have been suggested in the literature. When the components do not overlap each other signiﬁcantly in time and frequency, the phases of the original mixture spectrogram produce good synthesis quality [23]. In the mixture of piano and violin signals, signiﬁcant overlapping occurs between musical notes in the time domain but the degree of overlapping is relatively low in the frequency domain. Based on this observation, the phases of the original mixture spectrogram are used to reconstruct the source signals in this work. The reconstruction process can be summarised brieﬂy as follows. First, the phase information is added to each classiﬁed component to obtain its complex spectrum. Then the classiﬁed components from the above sections are combined to the individual source streams, and ﬁnally the inverse discrete Fourier Transform (IDFT) and the overlap-and-add technique are applied to obtain the time-domain signal. When the magnitude spectra are used as the basis functions, the frame-wise spectra are obtained as the product of the basis function with its gain. If the power spectra are used, a square root needs to be taken. If the frequency resolution is non-linear, additional processing is required for the re-synthesis using the IDFT.

3

Evaluations

Two music sources (played by two diﬀerent instruments, i.e. piano and violin) with diﬀerent number of notes overlapping each other in the time domain, were used to generate artiﬁcially an instantaneous mixture signal. The lengths of piano and violin source signals are both 20 seconds, containing 6 and 5 notes respectively. The K-NN classiﬁer constant K was selected as K = 30. The signalto-noise ratio (SNR), deﬁned as follows, was used to measure the quality of both the separated notes and the whole source signal, 2 s,t [Xm ]s,t SN R(m, j) = (8) 2 s,t ([Xm ]s,t − [Xj ]s,t ) where s and t are the row and column indices of the matrix respectively. The SNR was computed based on the magnitude spectrograms Xm and Xj of the mth reference and the j th separated component to prevent the reconstruction

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process from aﬀecting the quality [22]. For the same note, j = m. In general, higher SNR values represent better separation quality of the separated notes and source signals, vice-versa. The training database used in the classiﬁcation process was provided by the McGill University Master Samples Collection [16], University of Iowa website [21]. It contains 53 music signals with 29 of which are piano signals and the rest are violin signals. All the signals were sampled at 44100 Hz. The reference source signals were stored for the measurement of separation quality. For the purpose of training, the signals were ﬁrstly segmented into frames, and then the MFCC feature vectors were computed from these frames. In total,

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999 frames were computed for each signal. Figures 7 and 8 show the collection of the features from the typical piano and violin signals (i.e. “Piano.ﬀ.A0.wav” and “Violin.pizz.pp.sulG.C4B4.wav”) respectively. In both ﬁgures, it can be seen that there exist features whose coeﬃcients are all zeros due to the silence part of the signals. Before running the training algorithm, we performed feature selection by removing such frames of features. In the testing stage, the MFCC feature vectors of the individual music components that were separated by the NMF algorithm were calculated. Figure 9 shows the feature space of 15th separated component

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Fig. 12. The MSE values of the K nearest neighbors (i.e. the frames with the K minimal MSEs) are selected based on the K-NN clustering. In this experiment, K was set to 30.

(the ﬁnal component in our experiment). To determine whether this component belongs to piano or violin, we measured the mean squared error (MSE) between the feature space of the separated component and the feature spaces obtained from the training data. Figure 10 shows the MSEs between the feature vector of a frame (the ﬁnal frame in this experiment) of the separated component and those obtained in the training data. Then we sort the MSEs according their values along all these frames. The sorted MSEs are shown in Figure 11, where the frame indices were shuﬄed accordingly. After this, we applied the K-NN algorithm to obtain the 30 neighbors that are nearest to the separated component. The MSEs of these frames are shown in Figure 12. Their corresponding frame indices are shown in Figure 13, from which we can see that all the frame indices are greater

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Fig. 14. A separation example of the proposed system. (a) and (b) are the piano and violin sources respectively, (c) is the single channel mixture of these two sources, and (d) and (e) are the separated sources respectively. The vertical axes are the amplitude of the signals.

than 28971, which was the highest index number of the piano signals in the training data. As a result, this component was classiﬁed as a violin signal. Figure 14 shows a separation example of the proposed system, where (a) and (b) are the piano and violin sources respectively, (c) is the single channel mixture

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of these two sources, and (d) and (e) are the separated sources respectively. From this ﬁgure, we can observe that, although most notes are correctly separated and classiﬁed into the corresponding sources, there exist notes that were wrongly classiﬁed. The separated notes with the highest SNR is the ﬁrst note of the violin signal, for which the SNR equals to 9.7dB, while the highest SNR of the note within the piano signal is 6.4dB. The average SNRs for piano and violin are respectively 3.7 dB and 1.3 dB. According to our observation, the separation quality of the notes varies from notes to notes. In average, the separation quality of the piano signal is better than the violin signal.

4

Discussions

At the moment, for the separated components by the NMF algorithm, we calculate their MFCC features in the same way as for the signals in the training data. As a result, the evaluation of the MSEs becomes straightforward, which consequently facilitates the K-NN classiﬁcation. It is however possible to use the dictionary returned by the NMF algorithm (and possibly the activation coeﬃcients as well) as a set of features. In such a case, the NMF algorithm needs to be applied to the training data in the same way as the separated components obtained in the testing and classiﬁcation process. Similar to principal component analysis (PCA) which has been widely used to generate features in many classiﬁcation system, using NMF components directly as features has a great potential. As compared to using the MFCC features, the computational cost associated with the NMF features could be higher due to the iterations required for the NMF algorithms to converge. However, its applicability as a feature for classiﬁcation deserves further investigation in the future. Another important issue in applying NMF algorithms is the selection of the mode of the NMF model (i.e. the rank R). In our study, this determines the number of components that will be learned from the signal. In general, for a higher rank R, the NMF algorithm learns the components that are more likely corresponding to individual notes. However, there is a trade-oﬀ between the decomposition rank and the computational load, as a larger R incurs a higher computational cost. Also, it is known that NMF produces not only harmonic dictionary components but also sometimes ad-hoc spectral shapes corresponding to drums, transients, residual noise, etc. In our recognition system, these components were treated equally as the harmonic components. In other words, the feature vectors of these components were calculated and evaluated in the same way as the harmonic components. The ﬁnal decision was made from the labelling scores and the K-NN classiﬁcation results. We note that many classiﬁcation algorithms could also be applied for labelling the separated components, such as the Gaussian Mixture Models (GMMs), which have been used in both automatic speech/speaker recognition and music information retrieval. In this work, we choose the K-NN algorithm due its simplicity. Moreover, the performance of the single channel source separation system developed here is largely dependent on the separated components provided by the

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NMF algorithm. Although the music components obtained by the NMF algorithm are somehow sparse, their sparsity is not explicitly controlled. Also, we didn’t use the information from the music signals explicitly, such as the pitch information and harmonic structure. According to Li et al. [14], the information of pitch and common amplitude modulation can be used to improve the separation quality. Com

5

Conclusions

We have presented a new system for the single channel music sound separation problem. The system essentially integrates two techniques, automatic note decomposition using NMF, and note classiﬁcation based on the K-NN algorithm. A main assumption with the proposed system is that we have the prior knowledge about the type of instruments used for producing the music sounds. The simulation results show that the system produces a reasonable performance for this challenging source separation problem. Future works include using more robust classiﬁcation algorithm to improve the note classiﬁcation accuracy, and incorporating pitch and common amplitude modulation information into the learning algorithm to improve the separation performance of the proposed system.

References 1. Abdallah, S.A., Plumbley, M.D.: Polyphonic Transcription by Non-Negative Sparse Coding of Power Spectra. In: International Conference on Music Information Retrieval, Barcelona, Spain (October 2004) 2. Barry, D., Lawlor, B., Coyle, E.: Real-time Sound Source Separation: Azimuth Discrimination and Re-synthesis, AES (2004) 3. Brown, G.J., Cooke, M.P.: Perceptual Grouping of Musical Sounds: A Computational Model. J. New Music Res. 23, 107–132 (1994) 4. Casey, M.A., Westner, W.: Separation of Mixed Audio Sources by Independent Subspace Analysis. In: Proc. Int. Comput. Music Conf. (2000) 5. Devijver, P.A., Kittler, J.: Pattern Recognition - A Statistical Approach. Prentice Hall International, Englewood Cliﬀs (1982) 6. Every, M.R., Szymanski, J.E.: Separation of Synchronous Pitched Notes by Spectral Filtering of Harmonics. IEEE Trans. Audio Speech Lang. Process. 14, 1845– 1856 (2006) 7. Fevotte, C., Bertin, N., Durrieu, J.-L.: Nonnegative Matrix Factorization With the Itakura-Saito Divergence. With Application to Music Analysis. Neural Computation 21, 793–830 (2009) 8. FitzGerald, D., Cranitch, M., Coyle, E.: Extended Nonnegative Tensor Factorisation Models for Musical Sound Source Separation, Article ID 872425, 15 pages (2008) 9. Fukunage, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press Inc., London (1990)

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10. Gutierrez-Osuna, R.: Lecture 12: K Nearest Neighbor Classiﬁer, http://research.cs.tamu.edu/prism/lectures (accessed January 17, 2010) 11. Hoyer, P.: Non-Negative Sparse Coding. In: IEEE Workshop on Networks for Signal Processing XII, Martigny, Switzerland (2002) 12. Lee, D.D., Seung, H.S.: Learning the Parts of Objects by Non-Negative Matrix Factorization. Nature 401, 788–791 (1999) 13. Lee, D.D., Seung, H.S.: Algorithms for Non-negative Matrix Factorization. In: Neural Information Processing Systems, Denver (2001) 14. Li, Y., Woodruﬀ, J., Wang, D.L.: Monaural Musical Sound Separation Based on Pitch and Common Amplitude Modulation. IEEE Transactions on Audio, Speech, and Language Processing 17, 1361–1371 (2009) 15. Mellinger, D.K.: Event Formation and Separation in Musical Sound. PhD dissertation, Dept. of Comput. Sci., Standford Univ., Standford, CA (1991) 16. Opolko, F., Wapnick, J.: McGill University master samples, McGill Univ., Montreal, QC, Canada, Tech. Rep. (1987) 17. Pedersen, M.S., Wang, D.L., Larsen, J., Kjems, U.: Two-Microphone Separation of Speech Mixtures. IEEE Trans. on Neural Networks 19, 475–492 (2008) 18. Rickard, S., Balan, R., Rosca, J.: Real-time Time-Frequency based Blind Source Separation. In: 3rd International Conference on Independent Component Analysis and Blind Source Separation, San Diego, CA (December 2001) 19. Smaragdis, P., Brown, J.C.: Non-negative Matrix Factorization for Polyphonic Music Transcription. In: Proc. IEEE Int. Workshop Application on Signal Process. Audio Acoust., pp. 177–180 (2003) 20. Smaragdis, P.: Non-negative matrix factor deconvolution; extraction of multiple sound sources from monophonic inputs. In: Puntonet, C.G., Prieto, A.G. (eds.) ICA 2004. LNCS, vol. 3195, pp. 494–499. Springer, Heidelberg (2004) 21. The University of Iowa Musical Instrument Samples Database, http://theremin.music.uiowa.edu 22. Virtanen, T.: Sound Source Separation Using Sparse Coding with Temporal Continuity Objective. In: International Computer Music Conference, Singapore (2003) 23. Virtanen, T.: Separation of Sound Sources by Convolutive Sparse Coding. In: Proceedings of ISCA Tutorial and Research Workshop on Statistical and Perceptual Audio Processing, Jeju, Korea (2004) 24. Virtanen, T.: Sound Source Separation in Monaural Music Signals. PhD dissertation, Tampere Univ. of Technol., Tampere, Finland (2006) 25. Virtanen, T.: Monaural Sound Source Separation by Non-Negative Matrix Factorization with Temporal Continuity and Sparseness Criteria. IEEE Transactions on Audio, Speech, and Language Processing 15, 1066–1073 (2007) 26. Wang, D.L., Brown, G.J.: Computational Auditory Scene Analysis: Principles, Algorithms, and Applications. Wiley/IEEE Press (2006) 27. Wang, B., Plumbley, M.D.: Investigating Single-Channel Audio Source Separation Methods based on Non-negative Matrix Factorization. In: Nandi, Zhu (eds.) Proceedings of the ICA Research Network International Workshop, pp. 17–20 (2006) 28. Wang, B., Plumbley, M.D.: Single Channel Audio Separation by Non-negative Matrix Factorization. In: Digital Music Research Network One-day Workshop (DMRN+1), London (2006)

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29. Wang, W., Luo, Y., Chambers, J.A., Sanei, S.: Note Onset Detection via Non-negative Factorization of Magnitude Spectrum. EURASIP Journal on Advances in Signal Processing, Article ID 231367, 15 pages (June 2008); doi:10.1155/2008/231367 30. Wang, W., Cichocki, A., Chambers, J.A.: A Multiplicative Algorithm for Convolutive Non-negative Matrix Factorization Based on Squared Euclidean Distance. IEEE Transactions on Signal Processing 57, 2858–2864 (2009) 31. Webb, A.: Statistical Pattern Recognition, 2nd edn. Wiley, New York (2005) 32. Woodruﬀ, J., Pardo, B.: Using Pitch, Amplitude Modulation and Spatial Cues for Separation of Harmonic Instruments from Stereo Music Recordings. EURASIP J. Adv. Signal Process. (2007)

Notes on Nonnegative Tensor Factorization of the Spectrogram for Audio Source Separation: Statistical Insights and Towards Self-Clustering of the Spatial Cues C´edric F´evotte1,∗ and Alexey Ozerov2 1

CNRS LTCI, Telecom ParisTech - Paris, France [email protected] 2 IRISA, INRIA - Rennes, France [email protected]

Abstract. Nonnegative tensor factorization (NTF) of multichannel spectrograms under PARAFAC structure has recently been proposed by Fitzgerald et al as a mean of performing blind source separation (BSS) of multichannel audio data. In this paper we investigate the statistical source models implied by this approach. We show that it implicitly assumes a nonpoint-source model contrasting with usual BSS assumptions and we clarify the links between the measure of fit chosen for the NTF and the implied statistical distribution of the sources. While the original approach of Fitzgeral et al requires a posterior clustering of the spatial cues to group the NTF components into sources, we discuss means of performing the clustering within the factorization. In the results section we test the impact of the simplifying nonpoint-source assumption on underdetermined linear instantaneous mixtures of musical sources and discuss the limits of the approach for such mixtures. Keywords: Nonnegative tensor factorization (NTF), audio source separation, nonpoint-source models, multiplicative parameter updates.

1

Introduction

Nonnegative matrix factorization (NMF) is an unsupervised data decomposition technique with growing popularity in the ﬁelds of machine learning and signal/image processing [8]. Much research about this topic has been driven by applications in audio, where the data matrix is taken as the magnitude or power spectrogram of a sound signal. NMF was for example applied with success to automatic music transcription [15] and audio source separation [19,14]. The factorization amounts to decomposing the spectrogram data into a sum of rank-1 ∗

This work was supported in part by project ANR-09-JCJC-0073-01 TANGERINE (Theory and applications of nonnegative matrix factorization) and by the Quaero Programme, funded by OSEO, French State agency for innovation.

S. Ystad et al. (Eds.): CMMR 2010, LNCS 6684, pp. 102–115, 2011. c Springer-Verlag Berlin Heidelberg 2011

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spectrograms, each of which being the expression of an elementary spectral pattern amplitude-modulated in time. However, while most music recordings are available in multichannel format (typically, stereo), NMF in its standard setting is only suited to single-channel data. Extensions to multichannel data have been considered, either by stacking up the spectrograms of each channel into a single matrix [11] or by equivalently considering nonnegative tensor factorization (NTF) under a parallel factor analysis (PARAFAC) structure, where the channel spectrograms form the slices of a 3-valence tensor [5,6]. Let Xi be the short-time Fourier transform (STFT) of channel i, a complex-valued matrix of dimensions F × N , where i = 1, . . . , I and I is the number of channel (I = 2 in the stereo case). The latter approaches boil down to assuming that the magnitude spectrograms |Xi | are approximated by a linear combination of nonnegative rank-1 “elementary” spectrograms |Ck | = wk hTk such that |Xi | ≈

K

qik |Ck |

(1)

k=1

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K

qik wf k hnk

(2)

k=1

where {xif n } are the coeﬃcients of Xi . Introducing the nonnegative matrices Q = {qik }, W = {wf k }, H = {hnk }, whose columns are respectively denoted qk , wk and hk , the following optimization problem needs to be solved min d(|xif n ||ˆ vif n ) subject to Q, W, H ≥ 0 (3) Q,W,H

if n

with K def

vˆif n =

qik wf k hnk

(4)

k=1

and where the constraint A ≥ 0 means that the coeﬃcients of matrix A are nonnegative, and d(x|y) is a scalar cost function, taken as the generalized KullbackLeibler (KL) divergence in [5] or as the Euclidean distance in [11]. Complexˆ k are subsequently constructed using the phase of the valued STFT estimates C observations (typically, cˆkf n is given the phase of xif n , where i = argmax{qik }i [6]) and then inverted to produce time-domain components. The components pertaining to same “sources” (e.g, instruments) can then be grouped either manually or via clustering of the estimated spatial cues {qk }k . In this paper we build on these previous works and bring the following contributions :

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– We recast the approach of [5] into a statistical framework, based on a generative statistical model of the multichannel observations X. In particular we discuss NTF of the power spectrogram |X|2 with the Itakura-Saito (IS) divergence and NTF of the magnitude spectrogram |X| with the KL divergence. – We describe a NTF with a novel structure, that allows to take care of the clustering of the components within the decomposition, as opposed to after. The paper is organized as follows. Section 2 describes the generative and statistical source models implied by NTF. Section 3 describes new and existing multiplicative algorithms for standard NTF and for “Cluster NTF”. Section 4 reports experimental source separation results on musical data; we test in particular the impact of the simplifying nonpoint-source assumption on underdetermined linear instantaneous mixtures of musical sources and point out the limits of the approach for such mixtures. We conclude in Section 5. This article builds on related publications [10,3].

2 2.1

Statistical Models to NTF Models of Multichannel Audio

Assume a multichannel audio recording with I channels x(t) = [x1 (t), . . . , xI (t)]T , also referred to as “observations” or “data”, generated as a linear mixture of sound source signals. The term “source” refers to the production system, for example a musical instrument, and the term “source signal” refers to the signal produced by that source. When the intended meaning is clear from the context we will simply refer to the source signals as “the sources”. Under the linear mixing assumption, the multichannel data can be expressed as J x(t) = sj (t) (5) j=1

where J is the number of sources and sj (t) = [s1j (t), . . . sij (t), . . . , sIj (t)]T is the multichannel contribution of source j to the data. Under the common assumptions of point-sources and linear instantaneous mixing, we have sij (t) = sj (t) aij

(6)

where the coeﬃcients {aij } deﬁne a I×J mixing matrix A, with columns denoted [a1 , . . . , aJ ]. In the following we will show that the NTF techniques described in this paper correspond to maximum likelihood (ML) estimation of source and mixing parameters in a model where the point-source assumption is dropped and replaced by (i)

sij (t) = sj (t) aij (i)

(7)

where the signals sj (t), i = 1, . . . , I are assumed to share a certain “resemblance”, as modelled by being two diﬀerent realizations of the same random

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process, characterizing their time-frequency behavior, as opposed to be the same realization. Dropping the point-source assumption may also be viewed as ignoring some mutual information between the channels (assumption of sources contributing to each channel with equal statistics instead of contributing the same signal ). Of course, when the data has been generated from point-sources, dropping this assumption will usually lead to a suboptimal but typically faster separation algorithm, and the results section will illustrate this point. In this work we further model the source contributions as a sum of elementary components themselves, so that (i) (i) sj (t) = ck (t) (8) k∈Kj

where [K1 , . . . , KJ ] denotes a nontrivial partition of [1, . . . , K]. As will become (i) more clear in the following, the components ck (t) will be characterized by a spectral shape wk and a vector of activation coeﬃcients hk , through a statistical model. Finally, we obtain K (i) xi (t) = mik ck (t) (9) k=1

where mik is deﬁned as mik = aij if and only if k ∈ Kj . By linearity of STFT, model (8) writes equivalently xif n =

K

(i)

mik ckf n

(10)

k=1 (i)

(i)

where xif n and ckf n are the complex-valued STFTs of xi (t) and ck (t), and where f = 1, . . . , F is a frequency bin index and n = 1, . . . , N is a time frame index. 2.2

A Statistical Interpretation of KL-NTF

Denote V the I × F × N tensor with coeﬃcients vif n = |xif n | and Q the I × K matrix with elements |mik |. Let us assume so far for ease of presentation that J = K, i.e, mik = aik , so that M is a matrix with no particular structure. Then it can be easily shown that the approach of [5], brieﬂy described in Section 1 and consisting in solving min dKL (vf n |ˆ vif n ) subject to Q, W, H ≥ 0 (11) Q,W,H

if n

with vˆif n deﬁned by Eq. (4), is equivalent to ML estimation of Q, W and H in the following generative model : (i) |mik | |ckf n | (12) |xif n | = k (i) |ckf n |

∼ P(wf k hnk )

(13)

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where P(λ) denotes the Poisson distribution, deﬁned in Appendix A, and the KL divergence dKL (·|·) is deﬁned as dKL (x|y) = x log

x + y − x. y

(14)

The link between KL-NMF/KL-NTF and inference in composite models with Poisson components has been established in many previous publications, see, e.g, [2,12]. In our opinion, model (12)-(13) suﬀers from two drawbacks. First, the linearity of the mixing model is assumed on the magnitude of the STFT frames see Eq. (12) - instead of the frames themselves - see Eq. (10) -, which inherently (i) assumes that the components {ckf n }k have the same phase and that the mixing parameters {mik }k have the same sign, or that only one component is active in every time-frequency tile (t, f ). Second, the Poisson distribution is formally only deﬁned on integers, which impairs rigorous statistical interpretation of KL-NTF on non-countable data such as audio spectra. Given estimates Q, W and H of the loading matrices, Minimum Mean Square Error (MMSE) estimates of the component amplitudes are given by def (i) (i) |ckf n | = E{ |ckf n | | Q, W, H, |X| }

(15)

qik wf k hnk = |xif n | l qil wf l hnl

(16)

(i)

Then, time-domain components ck (t) are reconstructed through inverse-STFT (i) (i) of ckf n = |ckf n |arg(xif n ), where arg(x) denotes the phase of complex-valued x. 2.3

A Statistical Interpretation of IS-NTF

To remedy the drawbacks of the KL-NTF model for audio we describe a new model based on IS-NTF of the power spectrogram, along the line of [4] and also introduced in [10]. The model reads (i) xif n = mik ckf n (17) k (i)

ckf n ∼ Nc (0|wf k hnk )

(18)

where Nc (μ, σ 2 ) denotes the proper complex Gaussian distribution, deﬁned in Appendix A. Denoting now V = |X|2 and Q = |M|2 , it can be shown that ML estimation of Q, W and H in model (17)-(18) amounts to solving min dIS (vif n |ˆ vif n ) subject to Q, W, H ≥ 0 (19) Q,W,H

if n

where dIS (·|·) denotes the IS divergence deﬁned as dIS (x|y) =

x x − log − 1. y y

(20)

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Note that our notations are abusive in the sense that the mixing parameters |mik | and the components |ckf n | appearing through their modulus in Eq. (12) are in no way the modulus of the mixing parameters and the components appearing in Eq. (17). Similarly, the matrices W and H represent diﬀerent types of quantities in every case; in Eq. (13) their product is homogeneous to component magnitudes while in Eq. (18) their product is homogeneous to variances of comKL ponent variances. Formally we should have introduced variables |cKL , kf n |, W KL IS IS IS H to be distinguished from variables ckf n , W , H , but we have not in order to avoid cluttering the notations. The diﬀerence between these quantities should be clear from the context. Model (17)-(18) is a truly generative model in the sense that the linear mixing assumption is made on the STFT frames themselves, which is a realistic (i) assumption in audio. Eq. (18) deﬁnes a Gaussian variance model of ckf n ; the zero mean assumption reﬂects the property that the audio frames taken as the input of the STFT can be considered centered, for typical window size of about (i) 20 ms or more. The proper Gaussian assumption means that the phase of ckf n is assumed to be a uniform random variable [9], i.e., the phase is taken into the model, but in a noninformative way. This contrasts from model (12)-(13), which simply discards the phase information. Given estimates Q, W and H of the loading matrices, Minimum Mean Square Error (MMSE) estimates of the components are given by def

(i)

(i)

cˆkf n = E{ckf n | Q, W, H, X }

(21)

qik wf k hnk = xif n l qil wf l hnl

(22)

We would like to underline that the MMSE estimator of components in the STFT domain (21) is equivalent (thanks to the linearity of the STFT and its inverse) to Table 1. Statistical models and optimization problems underlaid to KL-NTF.mag and IS-NTF.pow KL-NTF.mag

Mixing model Comp. distribution Data Parameters Approximate Optimization

IS-NTF.pow Model (i) (i) |xif n | = k |mik | |ckf n | xif n = k mik ckf n (i) (i) |ckf n | ∼ P(wf k hnk ) ckf n ∼ Nc (0|wf k hnk ) ML estimation V = |X| V = |X|2 W, H, Q = |M| W, H, Q = |M|2 vˆif n = k qik wf k hnk min vif n ) min vif n ) if n dKL (vif n |ˆ if n dIS (vif n |ˆ

Q,W,H≥0

Q,W,H≥0

Reconstruction (i) |ckf n | =

q w h ik f k nk |xif n | l qil wf l hnl

(i)

cˆkf n =

q w h ik f k nk xif n l qil wf l hnl

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the MMSE estimator of components in the time domain, while the the MMSE estimator of STFT magnitudes (15) for KL-NTF is not consistent with time domain MMSE. Equivalence of an estimator with time domain signal squared error minimization is an attractive property, at least because it is consistent with a popular objective source separation measure such as signal to distortion ratio (SDR) deﬁned in [16]. The diﬀerences between the two models, termed “KL-NTF.mag” and “ISNTF.pow” are summarized in Table 1.

3 3.1

Algorithms for NTF Standard NTF

We are now left with an optimization problem of the form ˆ def min D(V|V) = d(vif n |ˆ vif n ) subject to Q, W, H ≥ 0 Q,W,H

(23)

if n

where vˆif n = k qik hnk wf k , and d(x|y) is the cost function, either the KL or IS divergence in our case. Furthermore we impose qk 1 = 1 and wk 1 = 1, so as to remove obvious scale indeterminacies between the three loading matrices Q, W and H. With these conventions, the columns of Q convey normalized mixing proportions (spatial cues) between the channels, the columns of W convey normalized frequency shapes and all time-dependent amplitude information is relegated into H. As common practice in NMF and NTF, we employ multiplicative algorithms ˆ These algorithms essentially consist of updating for the minimization of D(V|V). each scalar parameter θ by multiplying its value at previous iteration by the ratio of the negative and positive parts of the derivative of the criterion w.r.t. this parameter, namely ˆ − [∇θ D(V|V)] θ←θ , (24) ˆ + [∇θ D(V|V)] ˆ = [∇θ D(V|V)] ˆ + −[∇θ D(V|V)] ˆ − and the summands are both where ∇θ D(V|V) nonnegative [4]. This scheme automatically ensures the nonnegativity of the parameter updates, provided initialization with a nonnegative value. The derivative of the criterion w.r.t scalar parameter θ writes ˆ = ∇θ D(V|V) ∇θ vˆif n d (vif n |ˆ vif n ) (25) if n

where d (x|y) = ∇y d(x|y). As such, we get ˆ = ∇qik D(V|V) wf k hnk d (vif n |ˆ vif n )

(26)

fn

ˆ = ∇wf k D(V|V)

qik hnk d (vif n |ˆ vif n )

(27)

qik wf k d (vif n |ˆ vif n )

(28)

in

ˆ = ∇hnk D(V|V)

if

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109

We note in the following G the I ×F ×N tensor with entries gif n = d (vif n |ˆ vif n ). For the KL and IS cost functions we have x dKL (x|y) = 1 − (29) y 1 x dIS (x|y) = − 2 (30) y y Let A and B be F × K and N × K matrices. We denote A ◦ B the F × N × K tensor with elements af k bnk , i.e, each frontal slice k contains the outer product ak bTk .1 Now we note < S, T >KS ,KT the contracted product between tensors S and T, deﬁned in Appendix B, where KS and KT are the sets of mode indices over which the summation takes place. With these deﬁnitions we get ˆ = < G, W ◦ H >{2,3},{1,2} ∇Q D(V|V) ˆ = < G, Q ◦ H >{1,3},{1,2} ∇W D(V|V)

(31)

ˆ = < G, Q ◦ W >{1,2},{1,2} ∇H D(V|V)

(33)

(32)

and multiplicative updates are obtained as Q ← Q.

< G− , W ◦ H >{2,3},{1,2} < G+ , W ◦ H >{2,3},{1,2}

(34)

< G− , Q ◦ H >{1,3},{1,2} < G+ , Q ◦ H >{1,3},{1,2}

(35)

< G− , Q ◦ W >{1,2},{1,2} < G+ , Q ◦ W >{1,2},{1,2}

(36)

W ← W. H ← H.

The resulting algorithm can easily be shown to nonincrease the cost function at each iteration by generalizing existing proofs for KL-NMF [13] and for IS-NMF [1]. In our implementation normalization of the variables is carried out at the end of every iteration by dividing every column of Q by their 1 norm and scaling the columns of W accordingly, then dividing the columns of W by their 1 norm and scaling the columns of H accordingly. 3.2

Cluster NTF

For ease of presentation of the statistical composite models inherent to NTF, we have assumed in Section 2.2 and onwards that K = J, i.e., that one source sj (t) is one elementary component ck (t) with its own mixing parameters {aik }i . We now turn back to our more general model (9), where each source sj (t) is a sum of elementary components {ck (t)}k∈Kj sharing same mixing parameters {aik }i , i.e, mik = aij iﬀ k ∈ Kj . As such, we can express M as M = AL 1

(37)

This is similar to the Khatri-Rao product of A and B, which returns a matrix of dimensions F N × K with column k equal to the Kronecker product of ak and bk .

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where A is the I × J mixing matrix and L is a J × K “labelling matrix” with only one nonzero value per column, i.e., such that ljk = 1

iﬀ k ∈ Kj

(38)

ljk = 0

otherwise.

(39)

This speciﬁc structure of M transfers equivalently to Q, so that Q = DL

(40)

where D = |A| D = |A|

2

in KL-NTF.mag

(41)

in IS-NTF.pow

(42)

The structure of Q deﬁnes a new NTF, which we refer to as Cluster NTF, denoted cNTF. The minimization problem (23) is unchanged except for the fact that the minimization over Q is replaced by a minimization over D. As such, the derivatives w.r.t. wf k , hnk do not change and the derivatives over dij write ˆ = ∇dij D(V|V) ( ljk wf k hnk ) d (vif n |ˆ vif n ) (43) fn

=

k

k

ljk

wf k hnk d (vif n |ˆ vif n )

(44)

fn

i.e., ˆ =< G, W ◦ H >{2,3},{1,2} LT ∇D D(V|V)

(45)

so that multiplicative updates for D can be obtained as D ← D.

< G− , W ◦ H >{2,3},{1,2} LT < G+ , W ◦ H >{2,3},{1,2} LT

(46)

As before, we normalize the columns of D by their 1 norm at the end of every iteration, and scale the columns of W accordingly. In our Matlab implementation the resulting multiplicative algorithm for IScNTF.pow is 4 times faster than the one presented in [10] (for linear instantaneous mixtures), which was based on sequential updates of the matrices [qk ]k∈Kj , [wk ]k∈Kj , [hk ]k∈Kj . The Matlab code of this new algorithm as well as the other algorithms described in this paper can be found online at http://perso. telecom-paristech.fr/~fevotte/Samples/cmmr10/.

4

Results

We consider source separation of simple audio mixtures taken from the Signal Separation Evaluation Campaign (SiSEC 2008) website. More speciﬁcally, we used some “development data” from the “underdetermined speech and music mixtures task” [18]. We considered the following datasets :

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– wdrums, a linear instantaneous stereo mixture (with positive mixing coeﬃcients) of 2 drum sources and 1 bass line, – nodrums, a linear instantaneous stereo mixture (with positive mixing coeﬃcients) of 1 rhythmic acoustic guitar, 1 electric lead guitar and 1 bass line. The signals are of length 10 sec and sampled at 16 kHz. We applied a STFT with sine bell of length 64 ms (1024 samples) leading to F = 513 and N = 314. We applied the following algorithms to the two datasets : – KL-NTF.mag with K = 9, – IS-NTF.pow with K = 9,

Fig. 1. Mixing parameters estimation and ground truth. Top : wdrums dataset. Bottom : nodrums dataset. Left : results of KL-NTF.mag and KL-cNTF.mag; ground truth mixing vectors {|aj |}j (red), mixing vectors {dj }j estimated with KL-cNTF.mag (blue), spatial cues {qk }k given by KL-NTF.mag (dashed, black). Right : results of ISNTF.pow and IS-cNTF.pow; ground truth mixing vectors {|aj |2 }j (red), mixing vectors {dj }j estimated with IS-cNTF.pow (blue), spatial cues {qk }k given by IS-NTF.pow (dashed, black).

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– KL-cNTF.mag with J = 3 and 3 components per source, leading to K = 9, – IS-cNTF.pow with J = 3 and 3 components per source, leading to K = 9. Every four algorithm was run 10 times from 10 random initializations for 1000 iterations. For every algorithm we then selected the solutions Q, W and H yielding smallest cost value. Time-domain components were reconstructed as discussed in Section 2.2 for KL-NTF.mag and KL-cNTF.mag and as is in Section 2.3 for IS-NTF.pow and IS-cNTF.pow. Given these reconstructed components, source estimates were formed as follows : – For KL-cNTF.mag and IS-cNTF.pow, sources are immediately computed using Eq. (8), because the partition K1 , . . . , KJ is known. – For KL-NTF.mag and IS-NTF.pow, we used the approach of [5,6] consisting of applying the K-means algorithm to Q (with J clusters) so as to label every component k to a source j, and each of the J sources is then reconstructed as the sum of its assigned components. Note that we are here not reconstructing the original single-channel sources (1) (I) sj (t) but their multichannel contribution [sj (t), . . . , sj (t)] to the multichannel data (i.e, their spatial image). The quality of the source image estimates was assessed using the standard Signal to Distortion Ratio (SDR), source Image to Spatial distortion Ratio (ISR), Source to Interference Ratio (SIR) and Source to Artifacts Ratio (SAR) deﬁned in [17]. The numerical results are reported in Table 2. The source estimates may also be listened to online at http://perso.telecom-paristech.fr/~fevotte/Samples/cmmr10/. Figure 1 displays estimated spatial cues together with ground truth mixing matrix, for every method and dataset. Discussion. On dataset wdrums best results are obtained with IS-cNTF.pow. Top right plot of Figure 1 shows that the spatial cues returned by D reasonably ﬁt the original mixing matrix |A|2 . The slightly better results of IS-cNTF.pow compared to IS-NTF.pow illustrates the beneﬁt of performing clustering of the spatial cues within the decomposition as opposed to after. On this dataset KL-cNTF.mag fails to adequately estimate the mixing matrix. Top left plot of Figure 1 shows that the spatial cues corresponding to the bass and hi-hat are correctly captured, but it appears that two columns of D are “spent” on representing the same direction (bass, s3 ), suggesting that more components are needed to represent the bass, and failing to capture the drums, which are poorly estimated. KL-NTF.mag performs better (and as such, one spatial cue qk is correctly ﬁtted to the drums direction) but overly not as well as IS-NTF.pow and IS-cNTF.pow. On dataset nodrums best results are obtained with KL-NTF.mag. None of the other methods adequately ﬁts the ground truth spatial cues. KL-cNTF.mag suﬀers same problem than on dataset wdrums : two columns of D are spent on the bass. In contrast, none of the spatial cues estimated by IS-NTF.pow and IS-cNTF.pow accurately captures the bass direction, and sˆ1 and sˆ2 both

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Table 2. SDR, ISR, SIR and SAR of source estimates for the two considered datasets. Higher values indicate better results. Values in bold font indicate the results with best average SDR. wdrums s1 s2 s3 (Hi-hat) (Drums) (Bass) KL-NTF.mag SDR -0.2 0.4 17.9 ISR 15.5 0.7 31.5 SIR 1.4 -0.9 18.9 SAR 7.4 -3.5 25.7 KL-cNTF.mag SDR -0.02 -14.2 1.9 ISR 15.3 2.8 2.1 SIR 1.5 -15.0 18.9 SAR 7.8 13.2 9.2 IS-NTF.pow SDR 12.7 1.2 17.4 ISR 17.3 1.7 36.6 SIR 21.1 14.3 18.0 SAR 15.2 2.7 27.3 IS-cNTF.pow SDR 13.1 1.8 18.0 ISR 17.0 2.5 35.4 SIR 22.0 13.7 18.7 SAR 15.9 3.4 26.5

nodrums s1 s2 s3 (Bass) (Lead G.) (Rhythmic G.) KL-NTF.mag SDR 13.2 -1.8 1.0 ISR 22.7 1.0 1.2 SIR 13.9 -9.3 6.1 SAR 24. 2 7.4 2.6 KL-cNTF.mag SDR 5.8 -9.9 3.1 ISR 8.0 0.7 6.3 SIR 13.5 -15.3 2.9 SAR 8.3 2.7 9.9 IS-NTF.pow SDR 5.0 -10.0 -0.2 ISR 7.2 1.9 4.2 SIR 12.3 -13.5 0.3 SAR 7.2 3.3 -0.1 IS-cNTF.pow SDR 3.9 -10.2 -1.9 ISR 6.2 3.3 4.6 SIR 10.6 -10.9 -3.7 SAR 3.7 1.0 1.5

contain much bass and lead guitar.2 Results from all four methods on this dataset are overly all much worse than with dataset wdrums, corroborating an established idea than percussive signals are favorably modeled by NMF models [7]. Increasing the number of total components K did not seem to solve the observed deﬁciencies of the 4 approaches on this dataset.

5

Conclusions

In this paper we have attempted to clarify the statistical models latent to audio source separation using PARAFAC-NTF of the magnitude or power spectrogram. In particular we have emphasized that the PARAFAC-NTF does not optimally exploits interchannel redundancy in the presence of point-sources. This still may be suﬃcient to estimate spatial cues correctly in linear instantaneous mixtures, in particular when the NMF model suits well the sources, as seen from 2

The numerical evaluation criteria were computed using the bss eval.m function available from SiSEC website. The function automatically pairs source estimates with ground truth signals according to best mean SIR. This resulted here in pairing left, middle and right blue directions with respectively left, middle and right red directions, i.e, preserving the panning order.

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the results on dataset wdrums but may also lead to incorrect results in other cases, as seen from results on dataset nodrums. In contrast methods fully exploiting interchannel dependencies, such as the EM algorithm based on model (17)-(18) (i) with ckf n = ckf n in [10], can successfully estimates the mixing matrix in both datasets. The latter method is however about 10 times computationally more demanding than IS-cNTF.pow. In this paper we have considered a variant of PARAFAC-NTF in which the loading matrix Q is given a structure such that Q = DL. We have assumed that L is known labelling matrix that reﬂects the partition K1 , . . . , KJ . An important perspective of this work is to let the labelling matrix free and automatically estimate it from the data, either under the constraint that every column lk of L may contain only one nonzero entry, akin to a hard clustering, i.e., lk 0 = 1, or more generally under the constraint that lk 0 is small, akin to soft clustering. This should be made feasible using NTF under sparse 1 -constraints and is left for future work.

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10. Ozerov, A., F´evotte, C.: Multichannel nonnegative matrix factorization in convolutive mixtures for audio source separation. IEEE Transactions on Audio, Speech and Language Processing 18(3), 550–563 (2010), http://www.tsi.enst.fr/~fevotte/ Journals/ieee_asl_multinmf.pdf 11. Parry, R.M., Essa, I.: Estimating the spatial position of spectral components in audio. In: Rosca, J.P., Erdogmus, D., Pr´ıncipe, J.C., Haykin, S. (eds.) ICA 2006. LNCS, vol. 3889, pp. 666–673. Springer, Heidelberg (2006) 12. Shashua, A., Hazan, T.: Non-negative tensor factorization with applications to statistics and computer vision. In: Proc. 22nd International Conference on Machine Learning, pp. 792–799. ACM, Bonn (2005) 13. Shepp, L.A., Vardi, Y.: Maximum likelihood reconstruction for emission tomography. IEEE Transactions on Medical Imaging 1(2), 113–122 (1982) 14. Smaragdis, P.: Convolutive speech bases and their application to speech separation. IEEE Transactions on Audio, Speech, and Language Processing 15(1), 1–12 (2007) 15. Smaragdis, P., Brown, J.C.: Non-negative matrix factorization for polyphonic music transcription. In: IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA 2003) (October 2003) 16. Vincent, E., Gribonval, R., F´evotte, C.: Performance measurement in blind audio source separation. IEEE Transactions on Audio, Speech and Language Processing 14(4), 1462–1469 (2006), http://www.tsi.enst.fr/~fevotte/Journals/ ieee_asl_bsseval.pdf 17. Vincent, E., Sawada, H., Bofill, P., Makino, S., Rosca, J.P.: First stereo audio source separation evaluation campaign: Data, algorithms and results. In: Davies, M.E., James, C.J., Abdallah, S.A., Plumbley, M.D. (eds.) ICA 2007. LNCS, vol. 4666, pp. 552–559. Springer, Heidelberg (2007) 18. Vincent, E., Araki, S., Bofill, P.: Signal Separation Evaluation Campaign. In: (SiSEC 2008) / Under-determined speech and music mixtures task results (2008), http://www.irisa.fr/metiss/SiSEC08/SiSEC_underdetermined/ dev2_eval.html 19. Virtanen, T.: Monaural sound source separation by non-negative matrix factorization with temporal continuity and sparseness criteria. IEEE Transactions on Audio, Speech and Language Processing 15(3), 1066–1074 (2007)

A

Standard Distributions

Proper complex Gaussian Poisson

B

Nc (x|μ, Σ) = |π Σ|−1 exp −(x − μ)H Σ −1 (x − μ) x P(x|λ) = exp(−λ) λx!

Contracted Tensor Product

Let S be a tensor of size I1 × . . . × IM × J1 × . . . × JN and T be a tensor of size I1 × . . . × IM × K1 × . . . × KP . Then, the contracted product < S, T >{1,...,M},{1,...,M} is a tensor of size J1 × . . . × JN × K1 × . . . × KP , given by < S, T >{1,...,M },{1,...,M } =

I1 i1 =1

...

IM

si1 ,...,iM ,j1 ,...,jN ti1 ,...,iM ,k1 ,...,kP

(47)

iM =1

The contracted tensor product should be thought of as a form a generalized dot product of two tensors along common modes of same dimensions.

What Signal Processing Can Do for the Music Isabel Barbancho, Lorenzo J. Tard´ on, Ana M. Barbancho, Andr´es Ortiz, Simone Sammartino, and Cristina de la Bandera Grupo de Aplicaci´ on de las Tecnolog´ıas de la Informaci´ on y Comunicaciones, Departamento de Ingenier´ıa de Comunicaciones, E.T.S. Ingenier´ıa de Telecomunicaci´ on, Campus de Teatinos s/n, Universidad of M´ alaga, SPAIN [email protected] http://webpersonal.uma.es/~IBP/index.htm Abstract. In this paper, several examples of what signal processing can do in the music context will be presented. In this contribution, music content includes not only the audio ﬁles but also the scores. Using advanced signal processing techniques, we have developed new tools that will help us handling music information, preserve, develop and disseminate our cultural music assets and improve our learning and education systems. Keywords: Music Signal Processing, Music Analysis, Music Transcription, Music Information Retrieval, Optical Music Recognition, Pitch Detection.

1

Introduction

Signal Processing Techniques are a powerful set of mathematical tools that allow to obtain from a signal the required information for a certain purpose. Signal Processing Techniques can be used for any type of signal: communication signals, medical signals, Speech signals, multimedia signals, etc. In this contribution, we focus on the application of signal processing techniques to music information: audio and scores. Signal processing techniques can be used for music database exploration. In this ﬁeld, we present a 3D adaptive environment for music content exploration that allows the exploration of musical contents in a novel way. The songs are analyzed and a series of numerical descriptors are computed to characterize their spectral content. Six main musical genres are deﬁned as axes of a multidimensional framework, where the songs are projected. A three-dimensional subdomain is deﬁned by choosing three of the six genres at a time and the user is allowed to navigate in this space, browsing, exploring and analyzing the elements of this musical universe. Also, inside this ﬁeld of music database exploration, a novel method for music similarity evaluation is presented. The evaluation of music similarity is one of the core components of the ﬁeld of Music Information Retrieval (MIR). In this study, rhythmic and spectral analyses are combined to extract the tonal proﬁle of musical compositions and evaluate music similarity. Music signal processing can be used also for the preservation of the cultural heritage. In this sense, we have developed a complete system with an interactive S. Ystad et al. (Eds.): CMMR 2010, LNCS 6684, pp. 116–137, 2011. c Springer-Verlag Berlin Heidelberg 2011

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graphical user interface for Optical Music Recognition (OMR), specially adapted for scores written in white mensural notation. Color photographies of Ancient Scores taken at the Archivo de la Catedral de M´ alaga have been used as input to the system. A series of pre-processing steps are aimed to improve their quality and return binary images to be processed. The music symbols are extracted and classiﬁed, so that the system is able to transcribe the ancient music notation into modern notation and make it sound. Music signal processing can also be focused in developing tools for technologyenhanced learning and revolutionary learning appliances. In this sense, we present diﬀerent applications we have developed to help learning diﬀerent instruments: piano, violin and guitar. The graphical tool for piano learning we have developed, is able to detect if a person is playing the proper piano chord. The graphical tool shows to the user the time and frequency response of each frame of piano sound under analysis and a piano keyboard in which the played notes are highlighted as well as the name of the played notes. The core of the designed tool is a polyphonic transcription system able to detect the played notes, based on the use of spectral patterns of the piano notes. The designed tool is useful both for users with knowledge of music and users without these knowledge. The violin learning tool is based on a transcription system able to detect the pitch and duration of the violin notes and to identify the diﬀerent expressiveness techniques: d´etach´e with and without vibrato, pizzicato, tremolo, spiccato, flageolett-t¨ one. The interface is a pedagogical tools to aid in violin learning. For the guitar, we have developed a system able to perform in real time string and fret estimation of guitar notes. The system works in three modes: it is able to estimate the string and fret of a single note played on a guitar, strummed chords from a predeﬁned list and it is also able to make a free estimation if no information of what is being played is given. Also, we have developed a lightweight pitch detector for embedded systems to be used in toys. The detector is based on neural networks in which the signal preprocessing is a frequency analysis. The selected neural network is a perceptron-type network. For the preprocessing, the Goertzel Algorithm is the selected technique for the frequency analysis because it is a light alternative to FFT computing and it is very well suited when only few spectral points are enough to extract the relevant information. Therefore, the outline of the paper is as follows. In Section 2, musical content management related tools are presented. Section 3 is devoted to the presentation of the tool directly related to the preservation of the cultural heritage. Section 4 will present the diﬀerent tools developed for technology-enhanced music learning. Finally, the conclusions are presented in Section 5.

2

Music Content Management

The huge amount of digital musical content available through diﬀerent databases makes necessary to have intelligent music signal processing tools that help us managing all this information. In subsection 2.1 a novel tool for navigating through the music content is presented. This 3D navigation environment makes easier to look for inter-related

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musical contents and it also gives the opportunity to the user to get to know certain types of music that he would not have found with other more traditional ways of searching musical contents. In order to use a 3D environment as the one presented, or other types of methods for music information retrieval, the evaluation of music similarity is one of the core components. In subsection 2.2, the rhythmic and spectral analyses of music contents are combined to extract the tonal proﬁle of musical compositions and evaluate music similarity. 2.1 3D Environment for Music Content Exploration The interactive music exploration is an open problem [31], with increasing interest due to the growing possibilities to access large music data bases. Eﬀorts to automate and simplify the access to musical contents require to analyze the songs to obtain numerical descriptors in the time or frequency domains that can be used to measure and compare diﬀerences and similarities among them. We have developed an adaptive 3D environment that allows intuitive music exploration and browsing through its graphical interface. Music analysis is based on the use of the Mel frequency cepstral coeﬃcients (MFCCs) [27], a multidimensional space is built and each song is represented as a sphere in a 3D environment with tools to navigate, listen and query the music space. The MFCCs are essentially based on the short term Fourier transform. The windowed spectrum of the original signal is computed. A Mel bank of ﬁlters is applied to obtain a logarithmic frequency representation and the resulting spectrum is processed with a discrete cosine transform (DCT). At this end, the Mel coeﬃcients have to be clustered in few groups, in order to achieve a compact representation of the global spectral content of the signal. Here, the popular k-means clustering method has been employed and the centroid of the most populated cluster has been considered for a compact vectorial representation of the spectral meaning of the whole piece. This approach has been applied to a large number of samples for the six genres selected and a predominant vector has been computed for each genre. These vectors are considered as pseudoorthonormal coordinates reference vectors for the projection of the songs. In particular, for each song, the six coordinates have been obtained by computing the scalar product among the predominant vectors of the song itself and the ones of the six genres, conveniently normalized for unit norm. The graphical user interface comprises a main window, with diﬀerent functional panels (Figure 1). In the main panel, the representation of the songs in a 3D framework is shown: three orthogonal axes, representing the three selected genres are centered in the coordinates range and the set of songs are represented as blue spheres correspondingly titled. A set of other panels with diﬀerent functions complete the window. During the exploration of the space, the user is informed in real-time about the closest songs and can listen to them. 2.2 Evaluation of Music Similarity Based on Tonal Behavior The evaluation of music similarity is one of the core components of the ﬁeld of Music Information Retrieval (MIR). Similarity is often computed on the basis of

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Fig. 1. The graphical user interface for the 3D exploration of musical audio

the extraction of low-level time and frequency descriptors [25] or on the computation of rhythmic patterns [21]. Logan and Salomon [26] use the Mel Frequency Cepstral Coeﬃcients (MFCCs) as a main tool to compare audio tracks, based on their spectral content. Ellis et al. [13] adopt the cross-correlation of rhythmic patterns to identify common parts among songs. In this study, rhythmic and spectral analyses are combined to extract the tonal proﬁle of musical compositions and evaluate music similarity. The processing stage comprises two main steps: the computation of the main rhythmic meter of the song and the estimation of the distribution of contributions of tonalities to the overall tonal content of the composition. The calculus of the cross-correlation of the rhythmic pattern of the envelope of the raw signal allows a quantitative estimation of the main melodic motif of the song. Such temporal unit has to be employed as a base for the temporal segmentation of the signal, aimed to extract the pitch class proﬁle of the song [14] and, consequently, the vector of tonality contributions. Finally, this tonal behavior vector is employed as the main feature to describe the song and it is used to evaluate similarity. Estimation of the melodic cell. In order to characterize the main melodic motif of the track, the songs are analyzed to estimate the tempo. More than a real quantitative metrical calculus of the rhythmic pattern, the method aims at delivering measures for guiding the temporal segmentation of the musical signal, and at subsequently improving the representation of the song dynamics. This is aimed at optimizing the step for the computation of the tonal content of the audio signal, supplying the reference temporal frame for the audio windowing. The aim of the tempo induction is to estimate the width of the window used for windowing, so that the stage for the computation of the tonal content of the song

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attains improved performance. In particular, the window should be wide enough to include a single melodic cell, e.g.: a single chord. Usually, the distribution of tone contributions within a single melodic cell is uniform and coherent with the chord content. The chord notes are played once for each melodic cell, such that, by evaluating the tones content of each single cell, we can have a reliable idea of the global contribution of the single tonalities for the whole track. It is clear that there are exceptions for these assumptions, such as arpeggios, solos, etc. Both the width and the phase (temporal location) of the window are extremely important for achieving the best performance of the spectral analysis. A series of frequency analysis stages are performed on the raw signal in order to obtain the most robust estimate of the window. The signal is half-way rectiﬁed, low-pass ﬁltered and its envelope is computed. The main window value is assumed to be best estimated by the average temporal distance between the points of the ﬁrst order derivative of the envelope showing the highest diﬀerence among crests and troughs. The steps are schematically listed below: 1. The raw signal is half-way rectiﬁed and ﬁltered with a low-pass Butterworth ﬁlter, with a cut-oﬀ frequency of 100 Hz [12]. 2. The envelope of the ﬁltered signal is computed, using a low-pass Butterworth ﬁlter with a cut-oﬀ frequency of 1 Hz. 3. The ﬁrst order derivative is computed on the envelope. 4. The zero-crossing points of the derivative are found (the crests and the troughs of the envelope). 5. The diﬀerence between crests and troughs is computed and its empirical cumulative distribution is evaluated. 6. Only the values exceeding the 75th percentile of their cumulative distributions are kept. 7. The temporal distances among the selected troughs (or crests) are computed and the average value is calculated. A further fundamental parameter is the phase of the tempo detected. It assures the correct matching between the windowing of the signal and the extent of the melodic cell, which helps to minimize the temporal shifting. This is aimed by locating the position of the ﬁrst trough detected in the signal, as starting point for the windowing stage. The algorithm described has been employed to obtain an estimation of the melodic cell that is used in the subsequent steps of the computation of the tonal content. An objective evaluation of the performance of the method is hard to achieve because of the fuzzy perception of the main motif of the song by the human ear. Moreover, the exact usage of a strict regularity in metric is rarely found in modern music [20] and some slight variations in the rhythm throughout a whole composition are barely perceived by the listener. Nevertheless, a set of 30 songs have been selected from a set of 5 genres (Pop, Classic, Electro-Disco, Heavy Metals and Jazz). The songs have been analyzed by experienced listeners and the width of their main metric unity has been manually quantiﬁed. Then, the results obtained by the automatic procedure described have been compared.

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Table 1. Relative and absolute diﬀerences among the widths of the melodic window manually evaluated by the listeners and the ones automatically computed by the proposed algorithm Genre

Relative Diﬀerence Absolute Diﬀerence (percentage) (second)

Pop Classic Electro-Disco Heavy Metals Jazz

14.6 21.2 6.2 18.4 14.8

0.60 0.99 0.34 0.54 0.58

Mean

17.3

0.68

In Table 1, the diﬀerences between the widths of the window manually measured and automatically computed are shown. The best results are obtained for the Disco music tracks (6.2%), where the clear drummed bass background is well detected and the pulse coincides most of times with the tempo. The worst results are related to the lack of a clear driving bass in Classical music (21.2%), where the changes in time can be frequent and a uniform tempo measure is hardly detectable. However, the beats, or lower-level metrical features are, most of the times, submultiples of such tempo value, which make them usable for the melodic cell computation. Tonal behavior. Most of the music similarity systems aim at imitating the human perception of a song. This capacity is complex to analyze. The human brain carries out a series of subconscious processes, as the computation of the rhythm, the instruments richness, the musical complexity, the tonality, the mode, the musical form or structure, the presence of modulations, etc., even without any technical musical knowledge [29]. A novel technique for the determination of the tonal behavior of music signals based on the extraction of the pattern of tonality contributions is presented. The main process is based on the calculus of the contributions of each note of the chromatic scale (Pitch Class Profile - PCP), and the computation of the possible matching tonalities. The outcome is a vector reﬂecting the variation of the spectral contribution of each tonality throughout the entire piece. The song is time windowed with no overlapping windows, whose width is determined on the basis of the tempo induction algorithm. The Pitch Class Proﬁle is based on the contribution of the twelve semitone pitch classes to the whole spectrum. Fujishima [14] employed the PCPs as main ˙ tool for chord recognition, while Izmirli [22] deﬁned them as ‘Chroma Template’ and used them for audio key ﬁnding. Gomez and Herrera [16] applied machine learning methods to the ‘Harmonic Pitch Class Proﬁle’, to estimate the tonalities of polyphonic audio tracks. The spectrum of the whole audio is analyzed, and the distribution of the strengths of all the tones is evaluated. The diﬀerent octaves are grouped to measure the contribution of the 12 basic tones. A detailed description follows.

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The signal spectrum, computed by the discrete Fourier transform, is simpliﬁed making use of the MIDI numbers as in [8]. The PCP is a 12-dimensions vector (from C to B) obtained by the sum of the spectral amplitudes for each tone, spawning through the seven octaves (from C1 to B7, or 24 to 107 in MIDI number). That is, the ﬁrst element of the PCP vector is the sum of the strengths of the pitches from tone C1 to tone C7, the second one from tone C# 1 to tone C# 7, and so on. Each k-th element of the PCP vector, with k ∈ {1, 2, . . . , 12}, is computed as follows: 7 P CPt (k) = Xs (k + (i − 1) · 12) (1) i=1

where Xs is the simpliﬁed spectrum, the index k covers the twelve semitone pitches and i is used to index each octave. The subscript t stands for the temporal frame for which the PCP is computed. In order to estimate the predominant tonality of a track, it is important to deﬁne a series of PCPs for all the possible tonalities, to be compared with its own PCP. The shape of the PCP mainly depends on the modality of the tonality (Major or Minor). Hence, by assembling only two global proﬁles, for major and minor modes, and by shifting each of them twelve times according to the tonic pitch of the twelve possible tonalities of each mode, 24 tonalities proﬁles are obtained. Krumhansl [24] deﬁned the proﬁles empirically, on the base of a series of listening sessions carried out on a group of undergraduates from University of Harvard, who had to evaluate the correspondence among test tracks and probe tones. The author presented two global proﬁles, one for major and one for minor mode, representing the global contribution of each tone to all the tonalities for each mode. More recently, Temperley [35] presented a modiﬁed less biased version of Krumhansl proﬁles. In this context we propose a revised version of the Krumhansl’s proﬁles with the aim of avoiding the bias of the system for a particular mode. Basically, the two mode proﬁles are normalized to show the same sum of values and, then, their proﬁles are divided by their corresponding maximums. For each windowed frame of the track, the squared Euclidean distance between the PCP of the frame and each tonality proﬁle is computed to deﬁne a 24-elements vector. Each element of the vector is the sum of the squared diﬀerences between the amplitudes of the PCP and the tonality proﬁles. The squared distance is deﬁned as follow: ⎧ 11 ⎪ ⎪ [(PM (j + 1)) − P CPt ((j + k − 1) mod 12 + 1)]2 1 ≤ k ≤ 12 ⎨ j=0 Dt (k) = 11 ⎪ ⎪ [(Pm (j + 1)) − P CPt ((j + k − 1) mod 12 + 1)]2 13 ≤ k ≤ 24 ⎩ j=0

(2) where Dt (k) is the squared distance computed at time t of the k-th tonality, with k ∈ {1, 2, ..., 24}, and PM /Pm are, respectively, the major and minor proﬁle. The predominant tonality of each frame corresponds to the minimum of the distance vector Dt (k), where the index k, with k ∈ {1, ..., 12}, refers to the twelve major tonalities (from C to B) and k, with k ∈ {13, ..., 24}, refers

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Tonal behavior

Normalized Amplitude

1 0.8 0.6 0.4 0.2 0

C C# D Eb E F F# G Ab A Bb B c c# d d# e Tonalities

f

f# g g# a a# b

Fig. 2. An example of the tonal behavior of the Beatles’ song “I’ll be back”, where the main tonality is E major

to the twelve minor tonalities (from c to b). Usually major and minor tonalities are represented with capital and lower-case letter respectively. The empirical distribution of all the predominant tonalities, estimated throughout the entire piece, is calculated in order to represent the tonality contributions to the tonal content of the song. This is deﬁned as the ‘tonal behavior’ of the composition. In Figure 2, an example of the distribution of the tonality contributions for the Beatles’ song “I’ll be back” is shown. Music similarity. The vectors describing the tonal behavior of the songs are employed to measure their reciprocal degree of similarity. In fact the human brain is able to detect the main melodic pattern, even by means of subconscious processes and its perception of musical similarity is partially based on it [24]. The tonal similarity among the songs is computed by the Euclidean distance of the tonal vector calculated, following the equation: T SAB = TA − TB

(3)

where T SAB stands for the coeﬃcient of tonal similarity between the songs A and B and TA and TB are the empirical tonality distributions for song A and B, respectively. A robust evaluation of the performance of the proposed method for evaluation of music similarity is very hard to achieve. The judgment of the similarity among audio ﬁles is a very subjective issue, showing the complex reality of human perception. Nevertheless, a series of tests have been performed on some predetermined lists of songs. Four lists of 11 songs have been submitted to a group of ten listeners. They were instructed to sort the songs according to their perceptual similarity and tonal similarity. For each list, a reference song was deﬁned and the remaining 10 songs had to be sorted with respect to their degree of similarity with the reference one.

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A series of 10-element lists were returned by the users, as well as by the automatic method. Two kinds of experimental approaches were carried out: in the ﬁrst experiment, the users had to listen to the songs and sort them according to a global perception of their degree of similarity. In the second framework, they were asked to focus only on the tonal content. The latter was the hardest target to obtain, because of the complexity of discerning the parameters to be taken into account when listening to a song and evaluating its similarity with respect to other songs. The degree of coherence among the list manually sorted and the ones automatically processed was obtained. A weighted matching score for each pair of lists was computed, the reciprocal distance of the songs (in terms of the position index in the lists) was calculated. Such distances were linearly weighted, so that the ﬁrst songs in the lists reﬂected more importance than the last ones. In fact, it is easier to evaluate which is the most similar song among pieces that are similar to the reference one, than performing the same selection among very diﬀerent songs. The weights aid to compensate for this bias. Let Lα and Lβ represent two diﬀerent ordered lists of n songs, for the same reference song. The matching score C has been computed as follows: n |i − j| · ωi (4) C= i=1

where i and j are the indexes for lists Lα and Lβ , respectively, such that j is the index of the j-th song in list Lβ with Lα (i) ≡ Lβ (j). The absolute diﬀerence is linearly weighted by the weights ωi normalized such to sum to one, deﬁned by n the following expression: i=1 ωi = 1. Finally, the scores are transformed to be represented as percentage of the maximum score attainable. The eﬃciency of the automatic method was evaluated by measuring its coherence with the users’ response. The closer the two set of values, the better the performance of the automatic method. As expected, the evaluation of the automatic method in the ﬁrst experimental framework did not return reliable results because of the extreme deviation of the marks, due to the scarce relevance of the tones distribution in the subjective judgment of the song. As mentioned before, the tonal behavior of the song is only one of the parameters taken into account subconsciously by the human ear. Nevertheless, if the same songs were asked to be evaluated only by their tonal content, the scores drastically decreased, revealing the extreme lack of abstraction of the human ear. In Table 2 the results for both experimental frameworks are shown. The diﬀerences between the results of the two experiments are evident. Concerning the ﬁrst experiment, the mean score correspondence is 74.2%, among the users lists and 60.1% among the users and the automatic list. That is, the automatic method poorly reproduces the choices made by the users, taking into account a global evaluation of music similarity. Conversely, in the second experiment, better results were obtained. The mean correspondence score for the users’ lists decrease to 61.1%, approaching the value returned by the users and automatic list together, 59.8%. The performance of the system can be considered to be similar to the behavior of a mean human user, regarding the perception of tonal similarities.

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Table 2. Means and standard deviations of the correspondence scores obtained computing equation (4). The raws ‘Auto+Users’ and ‘Users’ refer to the correspondence scores computed among the users lists together with the automatic list and among only the users lists, respectively. The ‘Experiment 1’ is done listening and sorting the songs on the base of a global perception of the track, while ‘Experiment 2’ is performed trying to take into account only the tone distributions.

Lists List A

List B

List C

List D

Means

Method

Experiment 1 Mean St. Dev.

Experiment 2 Mean St. Dev.

Auto+Users

67.6

7.1

66.6

8.8

Users

72.3

13.2

57.9

11.5

Auto+Users

63.6

1.9

66.3

8.8

Users

81.8

9.6

66.0

10.5

Auto+Users

61.5

4.9

55.6

10.2

Users

77.2

8.2

57.1

12.6

Auto+Users

47.8

8.6

51.0

9.3

Users

65.7

15.4

63.4

14.4

Auto+Users

60.1

5.6

59.8

9.2

Users

74.2

11.6

61.1

12.5

Fig. 3. A snapshot of some of the main windows of interface of the OMR system

3

Cultural Heritage Preservation and Diﬀusion

Other important use of the music signal processing is the preservation and diffusion of the music heritage. In this sense, we have paid special attention to the

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musical heritage we have in the Archivo de la Catedral de M´ alaga. There, handwritten musical scores of the XVII-th and the early XVIII-th centuries written in white mensural notation are preserved. The aim of the tools we have developed is to give new life to that music, making easier to the people to get to know the music of that time. Therefore, in this section, the OMR system we have developed is presented. 3.1

A Prototype for an OMR System

OMR (Optical Music Recognition) systems are essentially based on the conversion of a digitalized music score into an electronic format. The computer must ‘read’ the document (in this case a manuscript), ‘interprete’ it and transcript its content (notes, time information, execution symbols etc.) into an electronic format. The task can be addressed to recover important ancient documents and to improve their availability to the music community. In the OMR framework, the recognition of ancient handwritten scores is a real challenge. The manuscripts are often in a very poor state of conservation, due to their age an their preservation. The handwritten symbols are not uniform and additive symbols can be found to be manually added a posteriori by other authors. The digital acquisition of the scores and the lighting conditions in the exposure can cause an incoherence in the background of the image. All these conditions make the development of an eﬃcient OMR system a very hard practice. Although the system workﬂow can be generalized, the speciﬁc algorithms cannot be blindly used for diﬀerent authors but it has to be trained for each use. We have developed a whole OMR system [34] for two styles of writing scores in white mensural notation. Figure 3 shows a snapshot of its graphical user interface. In the main window a series of tools are supplied to follow a complete workﬂow, based on a number of steps: the pre-processing of the image, the partition of the score into each single staﬀ and the processing of the staves with the extraction, the classiﬁcation and the transcription of the musical neums. Each tool corresponds to a individual window that allows the user to interact to complete the stage. The preprocessing of the image, aimed at feeding the system with the cleanest black and white image of the score, is divided into the following tasks: the clipping of region of interest of the image [9], the automatic blanking of red frontispieces, the conversion from RGB to grayscale, the compensation of the lighting conditions, the binarization of the image [17] and the correction of image tilt [36]. After partitioning the score into each single staﬀ, the staﬀ lines are tracked and blanked and the symbols are extracted and classiﬁed. In particular, a series of multidimensional feature vectors are computed on the geometrical extent of the symbols and a series of corresponding classiﬁers are employed to relate the neums to their correspondent musical symbols. In any moment, the interface allows the user for a careful following of each processing stage.

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127

Tools for Technology-Enhanced Learning and Revolutionary Learning Appliances

Music signal processing tools also make possible the development of new interactive methods for music learning using a computer or a toy. In this sense, we have developed a number of specialized tools to help to learn how to play piano, violin and guitar. These tools will be presented in sections 4.1, 4.2 and 4.3, respectively. It is worth to mention that for the development of these tools, the very special characteristics of the instruments have been taken into account. In fact, the people who have developed such tools are able to play these instruments. This has contributed to make these tools specially useful because in the development, we have observed the main diﬃculties of each of the instruments. Finally, thinking about developing toys, or other small embedded systems with musical intelligence, in subsection 4.4, we present a lightweight pitch detector that has been designed to this aim. 4.1

Tool for Piano Learning

The piano is a musical instrument that is widely used in all kinds of music and as an aid to the composition due to its versatility and ubiquity. This instrument is played by means of a keyboard and allows to get very rich polyphonic sound. Piano learning involves several diﬃculties that come from its great possibilities of generating sound with high polyphony number. These diﬃculties are easily observed when the musical skills are small or when trying to transcribe its sound when piano is used in composition. Therefore, it is useful to have a system that determines the notes that sound in a piano in each time frame and represent them in a simple form that can be easily understood, this is the aim of the tool that will be presented. The core of the designed tool is a polyphonic transcription system able to detect the played notes using spectral patterns of the piano notes [6], [4]. The approach used in the proposed tool to perform the polyphonic transcription is rather diﬀerent from the proposals that can be found in the literature [23]. In our case, the audio signal to be analyzed will be considered to have certain similarities to the code division multiple access CDMA communications signal. Our model will consider the spectral patterns of the diﬀerent piano notes [4]. Therefore, in order to perform the detection of the notes that sound during each time frame, we have considered a suitable modiﬁcation of a CDMA multiuser detection technique to cope with the polyphonic nature of the piano music and with the diﬀerent energy of the piano notes in the same way as an advanced CDMA receiver [5]. A snapshot of the main windows of the interface is presented in Figure 4. The designed graphical user interface is divided into three parts: – The management items of the tool are three main buttons: one button to adquire the piano music to analyze, another button to start the system and a ﬁnal button to reset the system. – The time and frequency response of each frame of piano sound under analysis are in the middle part of the window.

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Fig. 4. A snapshot of the main windows of the interface of the tool for piano learning

– A piano keyboard in which the played notes are highlighted as well as the name of the played notes is shown at the bottom. 4.2

Tool for Violin Learning

The violin is one of the most complex instruments often used by the children for the ﬁrst approach to music learning. The main characteristic of the violin is its great expressiveness due to the wide range of interpretation techniques. The system we have developed is able to detect not only the played pitch, as other transcription systems [11], but also the technique employed [8]. The signal envelope and the frequency spectrum are considered in time and frequency domain, respectively. The descriptors employed for the detection system have been computed analyzing a high amount of violin recordings, from the Musical Instrument Sound Data Base RWC-MDB-1-2001-W05 [18] and other home made recordings. Diﬀerent playing techniques have been performed in order to train the system for its expressiveness capability. The graphical interface is aimed to facilitate the violin learning for any user. For the signal processing tool, a graphical interface has been developed. The main window presents two options for the user, the theory section (Teor´ıa) and the practical section (Pr´ actica). In the section Teor´ıa the user is encouraged to learn all the concepts about the violin’s history, the violin’s parts and the playing posture (left and right hand), while the section Pr´ actica is mainly based on an expressiveness transcription system [8]. Here, the user starts with the ‘basic study’ sub-section, where the main violin positions are presented, illustrating the placement of the left hand on the ﬁngerboard, with the aim to attain a good intonation. Hence, the user can record the melody correspondence to the

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selected position and ask the application to correct it, returning the errors made. Otherwise, in the ‘free practice’ sub-section, any kind of violin recording can be analyzed for its melodic content, detecting the pitch, the duration of the notes and the techniques employed (e.g.:d´etach´e with and without vibrato, pizzicato, tremolo, spiccato, flageolett-t¨ one). The user can also visualize the envelope and the spectrum of the each note and listen to the MIDI transcription generated. In Figure 5, some snapshots of the interface are shown. The overall performance attained by our system in the detection and correction of the notes and expressiveness is 95.4%. 4.3

Tool for String and Fret Estimation in Guitar

The guitar is one of the most popular musical instruments nowadays. In contrast to other instruments like the piano, in the guitar the same note can be played plucking diﬀerent strings at diﬀerent positions. Therefore, the algorithms used for piano transcription [10] cannot be used for guitar. In guitar transcription it is important to estimate the string used to play a note [7].

Fig. 5. Three snapshots of the interface for violin learning are shown. Clockwise from top left: the main window, the analysis window and a plot of the MIDI melody.

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(a) Main window

(b) Single note estimation with tuner Fig. 6. Graphical interface of the tool for guitar learning

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The system presented in this demonstration is able to estimate the string and the fret of a single note played with a very low error probability. In order to keep a low error probability when a chord is strummed on a guitar, the system chooses which chord has been most likely played from a predeﬁned list. The system works with classical guitars as well as acoustic or electric guitars. The sound has to be captured with a microphone connected to the computer soundcard. It is also possible to plug a cable from an electric guitar to the sound card directly. The graphical interface consists of a main window (Figure 6(a)) with a pop-up menu where you can choose the type of guitar you want to use with the interface. The main window includes a panel (Estimaci´ on) with three push buttons, where you can choose between three estimation modes: – The mode Nota u ´nica (Figure 6(b)) estimates the string and fret of a single note that is being played and includes a tuner (afinador ). – The mode Acorde predeterminado estimates strummed chords that are being played. The system estimates the chord by choosing the most likely one from a predeﬁned list. – The last mode, Acorde libre, makes a free estimation of what is being played. In this mode the system does not have the information of how many notes are being played, so this piece of information is also estimated. Each mode includes a window that shows the microphone input, a window with the Fourier Transform of the sound sample, a start button, a stop button and an exit button (Salir ). At the bottom of the screen there is a panel that represents a guitar. Each row stands for a string on the guitar and the frets are numbered from one to twelve. The current estimation of the sound sample, either note or chord, is shown on the panel with a red dot. 4.4

Lightweight Pitch Detector for Embedded Systems Using Neural Networks

Pitch detection could be deﬁned as an act of listening to a music melody and writing down music notation of the piece, that is, to decide the notes played [28]. Basically, this is a pattern recognition problem over the time, where each pattern corresponds to features characterizing a musical note (e.g. the fundamental frequency). Nowadays, there exists a wide range of applications for pitch detection: educational applications, music-retrieval systems, automatic music analysis systems, music games, etc. The main problem of pitch detection systems is the computational complexity required, especially if they are polyphonic [23]. Artiﬁcial intelligence techniques often involve an eﬃcient and lightweight alternative for classiﬁcation and recognition tasks. These techniques can be used, in some cases, to avoid other processing algorithms, for downshifting the computational complexity, speeding up or improving the eﬃciency of the system [3], [33]. This is the case of audio processing and music transcription. When only a small amount of memory and processing power are available, FFT-based detection techniques can be too costly to implement. In this case,

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artiﬁcial intelligence techniques, such as neural networks sized up to be implemented in a small system, can provide the necessary accuracy. There are two alternatives [3], [33] in neural networks. The ﬁrst one is unsupervised training. This is the case of some networks which have been specially designed for pattern classiﬁcation such as self-organizative maps. However, the computational complexity of this implementation is too high for a low-cost microcontroller. The other alternative is supervised training neural networks. This is the case of perceptron-type networks. In these networks, the synaptic weights connecting each neuron are modiﬁed as a new training vector is presented. Once the network is trained, the weights can be statically stored to classify new network entries. The training algorithm can be done on a diﬀerent machine from the machine where the network propagation algorithm is executed. Hence the only limitation comes from the available memory. In the proposed system, we focus on the design of a lightweight pitch detection for embedded systems based on neural networks in which the signal preprocessing is a frequency analysis. The selected neural network is a perceptron-type network. For the preprocessing, the Goertzel algorithm [15] is the selected technique for the frequency analysis because it is a light alternative to FFT computing if we are only interested in some of the spectral points.

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Figure 7 shows the block diagram of the detection system. This ﬁgure shows the hardware connected to the microcontroller’s A/D input, which consists of a preampliﬁer in order to accommodate the input from the electret microphone into the A/D input range and an anti-aliasing ﬁlter. The anti-aliasing ﬁlter provides 58dB of attenuation at cutoﬀ which is enough to ensure the anti-aliasing function. After the ﬁlter, the internal A/D converter of the microcontroller is used. After conversion, a buﬀer memory is required in order to store enough samples for the preprocessing block. The output of the preprocessing block is used for pitch detection using a neural network. Finally, an I2C (Inter-Integrated Circuit) [32] interface is used for connecting the microcontroller with other boards. We use the open source Arduino environment [1] with the AVR ATMEGA168 microcontroller [2] for development and testing of the pitch detection implementation. The system will be conﬁgured to detect the notes between A3 (220Hz)

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and G#5 (830.6Hz), following the well-tempered scale, as it is the system mainly used in Western music. This range of notes has been selected because one of the applications of the proposed system is the detection of vocal music of children and adolescents. The aim of the preprocessing stage is to transform the samples of the audio signal from the time domain to the frequency domain. The Goertzel algorithm [15], [30] is a light alternative to FFT computing if the interest is focused only in some of the spectrum points, as in this case. Given the frequency range of the musical notes in which the system is going to work, along with the sampling restriction of the selected processor, the selected sampling frequency is fs = 4KHz and the number of input samples N = 400, that obtain a precision of 10Hz, which is suﬃcient for the pitch detection system. On the other hand, in the preprocessing block, the number of frequencies fk , in which the Goertzel p algorithm is computed, is 50 and are given according to fp = 440 · 2 12 Hz with p = −24, −23, ..., 0, ..., 24, 25, so that, each note in the range of interest have, at least, one harmonic and one subharmonic to improve the detection performance of notes with octave or perfect ﬁfth relation. Finally, the output of the preprocessing stage is a vector that contains the squared modulus of the 50 points of interest of the Goertzel algorithm: the points of the power spectrum of the input audio signal in the frequencies of interest. For the algorithm implemented using ﬁxed-point arithmetic, the execution time is less than 3ms on a 16 MIPS AVR microcontroller. The number of points of the Goertzel algorithm are limited by the available memory. The Eq. 5 shows the number of bytes required to implement the algorithm. N nbytes = 2 + 2N + m (5) 4 In this expression, m represents the number of desired frequency points. Thus with m = 50 points and N = 400, the algorithm requires 1900bytes of RAM memory for signal input/processing/output buﬀering. Since the microcontroller has 1024bytes of RAM memory, it is necessary to use an external high-speed SPI RAM memory in order to have enough memory for buﬀering audio samples. Once the Goertzel Algorithm has been performed and the points are stored in the RAM memory, a recognition algorithm has to be executed for pitch detection. A useful alternative to spectral processing techniques consist of using artiﬁcial intelligence techniques. We use a statically trained neural network storing the network weights vectors in a EEPROM memory. Thus, the network training is performed in a computer with the same algorithm implemented and the embedded system only runs the network. Figure 8 depicts the structure of the neural network used for pitch recognition. It is a multilayer feed-forward perceptron with a back-propagation training algorithm. In our approach, sigmoidal activation has been used for each neuron as well as no neuron bias. This provides a fuzzy set of values, yj , at the output of each neural layer. The fuzzy set is controlled by the shape factor, α, of the sigmoid function, which is set to 0.8, and it is applied to a threshold-based decision function. Hence, outputs below 0.5 does not activate output neurons while values

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above 0.5 activate output neurons. The neural network parameters such as the number of neurons in the hidden layer or the shape factor of the sigmoid function has been determined experimentally. The neural network has been trained by running the BPN (Back Propagation Neural Network) on a PC. Once the network convergence is achieved, the weight vectors are stored. Regarding the output layer of the neural network, we use ﬁve neurons to encode 24 diﬀerent outputs corresponding to each note in two octaves (A3 − G#5 notation). The training and the evaluation of the proposed system has been done using independent note samples taken from the Musical Instrument Data Base RWC [19]. The selected instruments have been piano and human voice. The training of the neural network has been performed using 27 samples for each note in the

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range of interest. Thus, we used 648 input vectors to train the network. This way, the network convergence was achieved with an error of 0.5%. In Figure 9, we show the learning characteristic of the network when simulating the network with the training vectors. At the same time, we show the validation test using 96 input vectors (4 per note) which corresponds to about 15% of new inputs. As shown in Figure 9, the inputs are correctly classiﬁed due to the small diﬀerence among the outputs for the ideal, learning and validation inputs.

5

Conclusions

Nowadays, it is a requirement that all types of information will be widely available in digital form in digital libraries together with intelligent techniques for the creation and management of the digital information, thus, contents will be plentiful, open, interactive and reusable. It becomes necessary to link contents, knowledge and learning in such a way that information will be produced, stored, handled, transmitted and preserved to ensure long term accessibility to everyone, regardless of the special requirements of certain communities (e-inclusion). Among the many diﬀerent types of information, music happens to be one of the more widely demanded due to its cultural interest, for entertainment or even due to therapeutic reasons. Through this paper, we have presented several application of music signal processing techniques. It is clear that the use of such tools can be very enriching from several points of views: Music Content Management, Cultural Heritage Preservation and Diﬀusion, Tools for Technology-Enhanced Learning and Revolutionary Learning Appliances, etc. Now, that we have the technology at our side in every moment: mobile phones, e-books, computers, etc., all these tools we have developed can be easily used. There are still a lot open issues and things that should be improved, but more and more, technology helps music.

Acknowledgments This work has been funded by the Ministerio de Ciencia e Innovaci´ on of the Spanish Government under Project No. TIN2010-21089-C03-02, by the Junta de Andaluc´ıa under Project No. P07-TIC-02783 and by the Ministerio de Industria, Turismo y Comercio of the Spanish Government under Project No. TSI-0205012008-117. The authors are grateful to the person in charge of the Archivo de la Catedral de M´ alaga, who allowed the utilization of the data sets used in this work.

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Speech/Music Discrimination in Audio Podcast Using Structural Segmentation and Timbre Recognition Mathieu Barthet , Steven Hargreaves, and Mark Sandler Centre for Digital Music, Queen Mary University of London, Mile End Road, London E1 4NS, United Kingdom {mathieu.barthet,steven.hargreaves,mark.sandler}@eecs.qmul.ac.uk http://www.elec.qmul.ac.uk/digitalmusic/

Abstract. We propose two speech/music discrimination methods using timbre models and measure their performances on a 3 hour long database of radio podcasts from the BBC. In the ﬁrst method, the machine estimated classiﬁcations obtained with an automatic timbre recognition (ATR) model are post-processed using median ﬁltering. The classiﬁcation system (LSF/K-means) was trained using two diﬀerent taxonomic levels, a high-level one (speech, music), and a lower-level one (male and female speech, classical, jazz, rock & pop). The second method combines automatic structural segmentation and timbre recognition (ASS/ATR). The ASS evaluates the similarity between feature distributions (MFCC, RMS) using HMM and soft K-means algorithms. Both methods were evaluated at a semantic (relative correct overlap RCO), and temporal (boundary retrieval F-measure) levels. The ASS/ATR method obtained the best results (average RCO of 94.5% and boundary F-measure of 50.1%). These performances were favourably compared with that obtained by a SVM-based technique providing a good benchmark of the state of the art. Keywords: Speech/Music Discrimination, Audio Podcast, Timbre Recognition, Structural Segmentation, Line Spectral Frequencies, K-means clustering, Mel-Frequency Cepstral Coeﬃcients, Hidden Markov Models.

1

Introduction

Increasing amounts of broadcast material are being made available in the podcast format which is deﬁned in reference [52] as a “digital audio or video ﬁle that is episodic; downloadable; programme-driven, mainly with a host and/or theme; and convenient, usually via an automated feed with computer software” (the word podcast comes from the contraction of webcast, a digital media ﬁle distributed over the Internet using streaming technology, and iPod, the portable media player by Apple). New technologies have indeed emerged allowing users

Correspondence should be addressed to Mathieu Barthet.

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to access audio podcasts material either online (on radio websites such as the one from the BBC used in this study: http://www.bbc.co.uk/podcasts), or oﬄine, after downloading the content on personal computers or mobile devices using dedicated services. A drawback of the podcast format, however, is its lack of indexes for individual songs and sections, such as speech. This makes navigation through podcasts a diﬃcult, manual process, and software built on top of automated podcasts segmentation methods would therefore be of considerable help for end-users. Automatic segmentation of podcasts is a challenging task in speech processing and music information retrieval since the nature of the content from which they are composed is very broad. A non-exhaustive list of type of content commonly found in podcast includes: spoken parts of various types depending on the characteristics of the speakers (language, gender, number, etc.) and the recording conditions (reverberation, telephonic transmission, etc.), music tracks often belonging to disparate musical genres (classical, rock, jazz, pop, electro, etc.) and which may include a predominant singing voice (source of confusion since the latter intrinsically shares properties with the spoken voice), jingles and commercials which are usually complex sound mixtures including voice, music, and sound eﬀects. One step of the process of automatically segmenting and annotating podcasts therefore is to segregate sections of speech from sections of music. In this study, we propose two computational models for speech/music discrimination based on structural segmentation and/or timbre recognition and evaluate their performances in the classiﬁcation of audio podcasts content. In addition to their use with audio broadcast material (e.g. music shows, interviews) as assessed in this article, speech/music discrimination models may also be of interest to enhance navigation into archival sound recordings that contain both spoken word and music (e.g. ethnomusicology interviews available on the online sound archive from the British Library: https://sounds.bl.uk/). If speech/music discrimination models ﬁnd a direct application in automatic audio indexation, they may also be used as a preprocessing stage to enhance numerous speech processing and music information retrieval tasks such as speech and music coding, automatic speaker recognition (ASR), chord recognition, or musical instrument recognition. The speech/music discrimination methods proposed in this study rely on timbre models (based on various features such as the line spectral frequencies [LSF], and the mel-frequency cepstral coeﬃcients [MFCC]), and machine learning techniques (K-means clustering and hidden Markov models [HMM]). The ﬁrst proposed method comprises an automatic timbre recognition (ATR) stage using the model proposed in [7] and [16] trained here with speech and music content. The results of the timbre recognition system are then postprocessed using a median ﬁlter to minimize the undesired inter-class switches. The second method utilizes the automatic structural segmentation (ASS) model proposed in [35] to divide the signal into a set of segments which are homogeneous with respect to timbre before applying the timbre recognition procedure. A database of classical music, jazz, and popular music podcasts from the BBC was manually annotated for training and testing purposes (approximately 2,5

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hours of speech and music). The methods were both evaluated at the semantic level to measure the accuracy of the machine estimated classiﬁcations, and at the temporal level to measure the accuracy of the machine estimated boundaries between speech and music sections. Whilst studies on speech/music discrimination techniques usually provide the ﬁrst type of evaluation (classiﬁcation accuracy), boundary retrieval performances are not reported to our knowledge, despite their interest. The results of the proposed methods were also compared with those obtained with a state-of-the-art’s speech/music discrimination algorithm based on support vector machine (SVM) [44]. The remainder of the article is organized as follows. In section 2, a review of related works on speech/music discrimination is proposed. Section 3, we give a brief overview of timbre research in psychoacoustics, speech processing and music information retrieval, and then describe the architecture of the proposed timbre-based methods. Section 4 details the protocols and databases used in the experiments, and speciﬁes the measures used to evaluate the algorithms. The results of the experiments are given and discussed in section 5. Finally, section 6 is devoted to the summary and the conclusions of this work.

2

Related Work

Speech/music discrimination is a special case of audio content classiﬁcation reduced to two classes. Most audio content classiﬁcation methods are based on the following stages: (i) the extraction of (psycho)acoustical variables aimed at characterizing the classes to be discriminated (these variables are commonly referred to as descriptors or features), (ii) a feature selection stage in order to further improve the performances of the classiﬁer, that can be either done a priori based on some heuristics on the disparities between the classes to discern, or a posteriori using an automated selection technique, and (iii) a classiﬁcation system relying either on generative methods modeling the distributions in the feature space, or discriminative methods which determine the boundaries between classes. The seminal works on speech/music discrimination by Saunders [46], and Scheirer and Slaney [48], developed descriptors quantifying various acoustical speciﬁcities of speech and music which were then widely used in the studies on the same subject. In [46], Saunders proposed ﬁve features suitable for speech/music discrimination and whose quick computation in the time domain directly from the waveform allowed for a real-time implementation of the algorithm; four of them are based on the zero-crossing rate (ZCR) measure (a correlate of the spectral centroid or center of mass of the power spectral distribution that characterize the dominant frequency in the signal [33]), and the other was an energy contour (or envelope) dip measure (number of energy minima below a threshold deﬁned relatively to the peak energy in the analyzed segment). The zero crossing rates were computed on a short-term basis (frame-by-frame) and then integrated on a longer-term basis with measures of the skewness of their distribution (standard deviation of the derivative, the third central moment about the mean, number of zero crossings exceeding a threshold, and the diﬀerence of the zero crossing

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samples above and below the mean). When both the ZCR and energy-based features were used jointly with a supervised machine learning technique relying on a multivariate-Gaussian classiﬁer, a 98% accuracy was obtained on average (speech and music) using 2.4 s-long audio segments. The good performance of the algorithm can be explained by the fact that the zero-crossing rate is a good candidate to discern unvoiced speech (fricatives) with a modulated noise spectrum (relatively high ZCR) from voiced speech (vowels) with a quasi-harmonic spectrum (relatively low ZCR): speech signals whose characteristic structure is a succession of syllabes made of short periods of fricatives and long periods of vowels present a marked rise in the ZCR during the periods of fricativity, which do not appear in music signals, which are largely tonal (this however depends on the musical genre which is considered). Secondly, the energy contour dip measure characterizes the diﬀerences between speech (whose systematic changeovers between voiced vowels and fricatives produce marked and frequent change in the energy envelope), and music (which tends to have a more stable energy envelope) well. However, the algorithm proposed by Saunders is limited in time resolution (2.4 s). In [48], Scheirer and Slaney proposed a multifeature approach and examined various powerful classiﬁcation methods. Their system relied on the 13 following features and, in some cases, their variance: 4 Hz modulation energy (characterizing the syllabic rate in speech [30]), the percentage of low-energy frames (more silences are present in speech than in music), the spectral rolloﬀ, deﬁned as the 95th percentile of the power spectral distribution (good candidate to discriminate voiced from unvoiced sounds), the spectral centroid (often higher for music with percussive sounds than for speech whose pitches stay in a fairly low range), the spectral ﬂux, which is a measure of the ﬂuctuation of the shortterm spectrum (music tends to have a higher rate of spectral ﬂux change than speech), the zero-crossing rate as in [46], the cepstrum resynthesis residual magnitude (the residual is lower for unvoiced speech than for voiced speech or music), and a pulse metric (indicating whether or not the signal contains a marked beat, as is the case in some popular music). Various classiﬁcation frameworks were tested by the authors, a multidimensional Gaussian maximum a posteriori (MAP) estimator as in [46], a Gaussian mixture model (GMM), a k-nearestneighbour estimator (k-NN), and a spatial partitioning scheme (k-d tree), and all led to similar performances. The best average recognition accuracy using the spatial partitioning classiﬁcation was of 94.2% on a frame-by-frame basis, and of 98.6% when integrating on 2.4 s long segments of sound, the latter results being similar to those obtained by Saunders. Some authors used extensions or correlates of the previous descriptors for the speech/music discrimination task such as the higher order crossings (HOC) which is the zero-crossing rate of ﬁltered versions of the signal [37] [20] originally proposed by Kedem [33], the spectral ﬂatness (quantifying how tonal or noisy a sound is) and the spectral spread (the second central moment of the spectrum) deﬁned in the MPEG-7 standard [9], and a rhythmic pulse computed in the MPEG compressed domain [32]. Carey et al. introduced the use of the fundamental frequency f0 (strongly correlated to the perceptual attribute of pitch) and its derivative in order to characterize

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some prosodic aspects of the signals (f0 changes in speech are more evenly distributed than in music where they are strongly concentrated about zero due to steady notes, or large due to shifts between notes) [14]. The authors obtained a recognition accuracy of 96% using the f0 -based features with a Gaussian mixture model classiﬁer. Descriptors quantifying the shape of the spectral envelope were also widely used, such as the Mel Frequency Cepstral Coeﬃcients (MFCC) [23] [25] [2], and the Linear Prediction Coeﬃcients (LPC) [23] [1]. El-Maleh et al. [20] used descriptors quantifying the formant structure of the spectral envelope, the line spectral frequencies (LSF), as in this study (see section 3.1). By coupling the LSF and HOC features with a quadratic Gaussian classiﬁer, the authors obtained a 95.9% average recognition accuracy with decisions made over 1 s long audio segments, procedure which performed slightly better than the algorithm by Scheirer and Slaney tested on the same dataset (an accuracy increase of approximately 2%). Contrary to the studies described above that relied on generative methods, Ramona and Richard [44] developed a discriminative classiﬁcation system relying on support vector machines (SVM) and median ﬁltering post-processing, and compared diverse hierarchical and multi-class approaches depending on the grouping of the learning classes (speech only, music only, speech with musical background, and music with singing voice). The most relevant features amongst a large collection of about 600 features are selected using the inertia ratio maximization with feature space projection (IRMFSP) technique introduced in [42] and integrated on 1 s long segments. The method provided an F-measure of 96.9% with a feature vector dimension of 50. Those results represent an error reduction of about 50% compared to the results gathered by the French ESTER evaluation campaign [22]. As will be further shown in section 5, we obtained performances favorably comparable to those provided by this algorithm. Surprisingly, all the mentioned studies evaluated the speech/music classes recognition accuracy, but none, to our knowledge, evaluated the boundary retrieval performance commonly used to evaluate structural segmentation algorithms [35] (see section 4.3), which we also investigate in this work.

3

Classification Frameworks

We propose two audio classiﬁcation frameworks based on timbre models applied in this work to the speech/music discrimination task. The architecture of both systems are represented in Figure 1. The ﬁrst system (see Figure 1(a)) is based on the automatic timbre recognition (ATR) algorithm described in [7], initially developed for musical instrument recognition, and a post-processing step aiming at reducing the undesired inter-class switches (smoothing by median ﬁltering). This method will be denoted ATR. The second system (see Figure 1(b)) was designed to test whether the performances of the automatic timbre recognition system would be improved by using a pre-processing step which divides the signal into segments of homogenous timbre. To address this issue, the signal is ﬁrst processed with an automatic structural segmentation (ASS) procedure [35]. Automatic timbre recognition (ATR) is then applied to the retrieved segments

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Testing audio

Structural segmentation

S, D

Testing audio homogeneous segments

Timbre recognition

LSF, K, L

intermediate classification (short-term)

Post-processsing (median filtering)

W

segment-level classification

(a) Classiﬁcation based on automatic timbre recognition (ATR)

Timbre recognition

LSF, K, L

intermediate classification (short-term)

Post-processsing (class decision)

segment-level classification

(b) Classiﬁcation based on automatic structural segmentation and timbre recognition (ASS/ATR)

Fig. 1. Architecture of the two proposed audio segmentation systems. The tuning parameters of the systems’ components are also reported: number of line spectral frequencies (LSF), number of codevectors K, latency L for the automatic timbre recognition module, size of the sliding window W used in the median ﬁltering (post-processing), maximal number S of segment types, and minimal duration D of segments for the automatic structural segmentation module.

and the segment-level classiﬁcation decisions are obtained after a post-processing step whose role is to determine the classes most frequently identiﬁed within the segments. This method will be denoted ASS/ATR. In the remainder of this section we will ﬁrst present the various acoustical correlates of timbre used by the systems, and then describe both methods in more detail. 3.1

Acoustical Correlates of Timbre

The two proposed systems rely on the assumption that speech and music can be discriminated based on their diﬀerences in timbre. Exhaustive computational models of timbre have not yet been found and the common deﬁnition used by scholars remains vague: “timbre is that attribute of auditory sensation in terms of which a listener can judge that two sounds similarly presented and having the same loudness and pitch are dissimilar; Timbre depends primarily upon the spectrum of the stimulus, but it also depends on the waveform, the sound pressure, the frequency location of the spectrum, and the temporal characteristics of the stimulus.” [3]. Research in psychoacoustics [24] [10], [51], analysis/synthesis [45],

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music perception [4] [5], speech recognition [19], and music information retrieval [17] have however developed acoustical correlates of timbre characterizing some of the facets of this complex and multidimensional variable. The Two-fold Nature of Timbre: from Identity to Quality. One of the pioneers on timbre research, French researcher and electroacoustic music composer Schaeﬀer, put forward a relevant paradox about timbre wondering how a musical instrument’s timbre could be deﬁned considering that each of its tones also possessed a speciﬁc timbre [47]. Cognitive categorization’s theories shed light on Schaeﬀer’s paradox showing that sounds (objects, respectively) could be categorized either in terms of the sources from which they are generated, or simply as sounds (objects, respectively), in terms of the properties that characterize them [15]. These principles have been applied to timbre by Handel who described timbre perception as being both guided by our ability to recognize various physical factors that determine the acoustical signal produced by musical instruments [27] (later coined “source” mode of timbre perception by Hadja et al. [26]), and by our ability to analyze the acoustic properties of sound objects perceived by the ear, traditionally modeled as a time-evolving frequency analyser (later coined as “interpretative” mode of timbre perception in [26]). In order to refer to that two-fold nature of timbre, we like to use the terms timbre identity and timbre quality, which were proposed in reference [38]. The timbre identity and quality facets of timbre perception have several properties: they are not independent but intrinsically linked together (e.g. we can hear a guitar tone and recognize the guitar, or we can hear a guitar tone and hear the sound for itself without thinking to the instrument), they are function of the sounds to which we listen to (in some music like the “musique concr`ete”, the sound sources are deliberately hidden by the composer, hence the notion of timbre identity is diﬀerent, it may refer to the technique employed by the musician, e.g. a speciﬁc ﬁlter), they have variable domain of range (music lovers are often able to recognize the performer behind the instrument extending the notion of identity to the very start of the chain of sound production, the musician that controls the instrument). Based on these considerations, we include the notion of sound texture, as that produced by layers of instruments in music, into the deﬁnitions of timbre. The notion of timbre identity in music may then be closely linked to a speciﬁc band, a sound engineer, or the musical genre, largely related to the instrumentation). The Formant Theory of Timbre. Contrary to the classical theory of musical timbre advocated in the late 19th century by Helmholtz [29], timbre does not only depend on the relative proportion between the harmonic components of a (quasi-)harmonic sound; two straightforward experiments indeed show that timbre is highly altered when a sound a) is reversed in time, b) is pitch-shifted by frequency translation of the spectrum, despite the fact that in both cases the relative energy ratios between harmonics are kept. The works from the phonetician Slawson proved that the timbre of voiced sounds was mostly characterized by the invariance of their spectral envelope through pitch changes, and therefore

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a mostly ﬁxed formant1 structure, i.e. zones of high spectral energy (however, in the case of large pitch changes the formant structure needs to be slightly shifted for the timbral identity of the sounds to remain unchanged): “The popular notion that a particular timbre depends upon the presence of certain overtones (if that notion is interpreted as the “relative pitch” theory of timbre) is seen [...] to lead not to invariance but to large diﬀerences in musical timbre with changes in fundamental frequency. The “ﬁxed pitch” or formant theory of timbre is seen in those same results to give much better predictions of the minimum diﬀerences in musical timbre with changes in fundamental frequency. The results [...] suggest that the formant theory may have to be modiﬁed slightly. A precise determination of minimum diﬀerences in musical timbre may require a small shift of the lower resonances, or possibly the whole spectrum envelope, when the fundamental frequency changes drastically.” [49]. The ﬁndings by Slawson have causal and cognitive explanations. Sounds produced by the voice (spoken or sung) and most musical instruments present a formant structure closely linked to resonances generated by one or several components implicated in their production (e.g. the vocal tract for the voice, the body for the guitar, the mouthpiece for the trumpet). It seems therefore legitimate from the perceptual point of view to suggest that the auditory system relies on the formant structure of the spectral envelope to discriminate such sounds (e.g. two distinct male voices of same pitch, loudness, and duration), as proposed by the “source” or identity mode of timbre perception hypothesis mentioned earlier. The timbre models used in this study to discriminate speech and music rely on features modeling the spectral envelope (see the next section). In these timbre models, the temporal dynamics of timbre are captured up to a certain extent by performing signal analysis on successive frames where the signal is assumed to be stationary, and by the use of hidden markov model (HMM), as described in section 3.3. Temporal (e.g. attack time) and spectro-temporal parameters (e.g. spectral ﬂux) have also shown to be major correlates of timbre spaces but these ﬁndings were obtained in studies which did not include speech sounds but only musical instrument tones either produced on diﬀerent instruments (e.g. [40]), or within the same instrument (e.g. [6]). In situations where we discriminate timbres from various sources either implicitly (e.g. in everyday life’s situations) or explicitly (e.g. in a controlled experiment situation), it is most probable that the auditory system uses diﬀerent acoustical clues depending on the typological diﬀerences of the considered sources. Hence, the descriptors used to account for timbre diﬀerences between musical instruments’ tones may not be adapted for the discrimination between speech and music sounds. If subtle timbre diﬀerences are possible within a same instrument, large timbre diﬀerences are expected to occur between disparate classes, such as speech, and music, and those are liable to be captured by spectral envelope correlates. Music generally being a mixture of musical instrument sounds playing either synchronously in a polyphonic way, or solo, may exhibit complex formant structures induced by its 1

In this article, a formant is considered as being a broad band of enhanced power present within the spectral envelope.

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individual components (instruments), as well as the recording conditions (e.g. room acoustics). Some composers like Schoenberg have explicitly used very subtle instrumentation rules to produce melodies that were not shaped by changes of pitches like in traditional Western art music but by changes of timbre (the latter were called Klangfarben melodie by Schoenberg, which literally means “color melodies”). Hence, if they occur, formant structures in music are likely to be much diﬀerent from those produced by the vocal system. However, some intrinsic cases of confusions consist in music containing a predominant singing voice (e.g. in opera or choral music) since singing voice shares timbral properties with the spoken voice. The podcast database with which we tested the algorithms included such types of mixture. Conversely, the mix of a voice with a strong musical background (e.g. in commercials, or jingles) can also be a source of confusion in speech/music discrimination. This issue is addressed in [44], but not directly in this study. Sundberg [50] showed the existence of a singers or singing formant around 3 kHz when analyzing performances by classically trained male singers, which he attributed to a clustering of the third, fourth and ﬁfth resonances of the vocal tract. This diﬀerence between spoken and sung voices can potentially be captured by features charactering the spectral envelope, as the ones presented in next section. Spectral Envelope Representations: LP, LSF, and MFCC. Spectral envelopes can be obtained either from linear prediction (LP) [31] or from melfrequency cepstral coeﬃcients (MFCC) [17] which both oﬀer a good representation of the spectrum while keeping a small amount of features. Linear prediction is based on the source-ﬁlter model of sound production developed for speech coding and synthesis. Synthesis based on linear prediction coding is performed by processing an excitation signal (e.g. modeling the glottal excitation in the case of voice production) with an all-pole ﬁlter (e.g. modeling the resonances of the vocal tract in the case of voice production). The coeﬃcients of the ﬁlter are computed on a frame-by-frame basis from the autocorrelation of the signal. The frequency response of the LP ﬁlter hence represents the short-time spectral envelope. Itakura derived from the coeﬃcients of the inverse LP ﬁlter a set of features, the line spectral frequencies (LSF), suitable for eﬃcient speech coding [31]. The LSF have the interesting property of being correlated in a pairwise manner to the formant frequencies: two adjacent LSF localize a zone of high energy in the spectrum. The automatic timbre recognition model described in section 3.2 exploits this property of the LSF. MFCCs are computed from the logarithm of the spectrum computed on a Mel-scale (a perceptual frequency scale emphasizing low frequencies), either by taking the inverse Fourier transform, or a discrete cosine transform. A spectral envelope can be represented by considering the ﬁrst 20 to 30 MFCC coeﬃcients. In [51], Terasawa et al. have established that MFCC parameters are a good perceptual representation of timbre for static sounds. The automatic structural segmentation technique, component of the second classiﬁcation method described in section 3.3, was employed using MFCC features.

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147

Classification Based on Automatic Timbre Recognition (ATR)

The method is based on the timbre recognition system proposed in [7] and [16], which we describe in the remainder of this section. Feature Extraction. The algorithm relies on a frequency domain representation of the signal using short-term spectra (see Figure 2). The signal is ﬁrst decomposed into overlapping frames of equal size obtained by multiplying blocks of audio data with a Hamming window to further minimize spectral distortion. The fast Fourier transform (FFT) is then computed on a frame-by-frame basis. The LSF features described above are extracted using the short-term spectra. Classifier. The classiﬁcation process is based on the unsupervised K-means clustering technique both at the training and the testing stages. The principle of K-means clustering is to partition n-dimensional space (here the LSF feature space) into K distinct regions (or clusters), which are characterized by their centres (called codevectors). The collection of the K codevectors (LSF vectors) constitutes a codebook, whose function, within this context, is to capture the most relevant features to characterize the timbre of an audio signal segment. Hence, to a certain extent, the K-means clustering can here be viewed both as a classiﬁer and a technique of feature selection in time. The clustering of the feature space is performed according to the Linde-Buzo-Gray (LBG) algorithm [36].

Fig. 2. Automatic timbre recognition system based on line spectral frequencies and K-means clustering

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During the training stage, each class is attributed an optimized codebook by performing the K-means clustering on all the associated training data. During the testing stage, the K-means clustering is applied to blocks of audio data or decision horizons (collection of overlapping frames), the duration of which can be varied to modify the latency L of the classiﬁcation (see Figure 2). The intermediate classiﬁcation decision is obtained by ﬁnding the class which minimizes a codebook-to-codebook distortion measure based on the Euclidean distance [16]. As will be discussed in section 4.1, we tested various speech and music training class taxonomies (e.g. separating male and female voice for the speech class) to further enhance the performance of the recognition. Post-processing. Given that one of our ultimate goals is to be able to accurately locate the temporal start and end positions of speech and music sections, relatively short duration of decision horizons are required (a 1 s latency was used in the experiments). A drawback with this method though is that even if the LSF/K-means based algorithm achieves high levels of class recognition accuracy (for example, it might correctly classify music sections 90% of the time, see section 5), there can be undesirable switches from one type of retrieved class to another. This sometimes rapid variation between speech and music classiﬁcations makes it diﬃcult to accurately identify the start and end points of speech and music sections. Choosing longer classiﬁcation intervals though decreases the resolution with which we are able to pinpoint any potential start or end time. In an attempt to alleviate this problem, we performed some post-processing on the initial results obtained with the LSF/K-means based algorithm. All “music” sections are attributed a numerical class index of 0, and all “speech” sections a class index of 1. The results are resampled at 0.1 s intervals and then processed through a median ﬁlter. Median ﬁltering is a nonlinear digital ﬁltering technique which has been widely used in digital image processing and speech/music information retrieval to remove noise, e.g. in the peak-picking stage of an onset detector in [8], or for the same purposes as in this work, to enhance speech/music discrimination in[32], and [44]. Median ﬁltering has the eﬀect of smoothing out regions of high variation. The size W of the sliding window used in the median ﬁltering process was empirically tuned (see section 5). Contiguous short-term classiﬁcations of same types (speech or music) are then merged together to form segment-level classiﬁcations. Figure 3 shows a comparison between podcasts’ ground truth annotations and typical results of classiﬁcation before and after post-processing. Software Implementation. The intermediate classiﬁcation decisions were obtained with the Vamp [13] musical instrument recognition plugin [7] trained for music and speech classes. The plugin works interactively with the Sonic Visualiser host application developed to analyse and visualise music-related information from audio ﬁles [12]. The latency L of the classiﬁcation (duration of the decision horizon) can be varied between 0.5 s and 10 s. In this study, we used a 1 s long latency in order to keep a good time resolution/performance ratio [7]. The median ﬁltering post-processing was performed in Matlab. An example of

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Fig. 3. Podcast ground truth annotations (a), classiﬁcation results at 1 s intervals (b) and post-processed results (c)

detection of a transition from a speech to a music part within Sonic Visualiser is shown in Figure 4. 3.3

Classification Based on Automatic Structural Segmentation and Timbre Recognition (ASS/ATR)

Automatic Structural Segmentation. We used the structural segmentation technique based on constrained clustering initially proposed in [35] for automatic music structure extraction (chorus, verse, etc.). The technique is thoroughly described in [21], study in which it is applied to studio recordings’ intelligent editing. The technique relies on the assumption that the distributions of timbre features are similar over music structural elements of same type. The high-level song structure is hence determined upon structural/timbral similarity. In this study we extend the application of the technique to audio broadcast content (speech and music parts) without focusing on the structural ﬂuctuations within the parts themselves. The legitimacy to port the technique to speech/music

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Fig. 4. Example of detection of a transition between speech and music sections in a podcast using the Vamp timbre recognition transform jointly with Sonic Visualiser

discrimination relies on the fact that a higher level of similarity is expected between the various spoken parts one one hand, and between the various music parts, on the other hand. The algorithm, implemented as a Vamp plugin [43], is based on a frequencydomain representation of the audio signal using either a constant-Q transform, a chromagram or mel-frequency cepstral coeﬃcients (MFCC). For the reasons mentioned earlier in section 3.1, we chose the MFCCs as underlying features in this study. The extracted features are normalised in accordance with the MPEG7 standard (normalized audio spectrum envelope [NASE] descriptor [34]), by expressing the spectrum in the decibel scale and normalizing each spectral vector by the root mean square (RMS) energy envelope. This stage is followed by the extraction of 20 principal components per block of audio data using principal component analysis. The 20 PCA components and the RMS envelope constitute a sequence of 21 dimensional feature vectors. A 40-state hidden markov model (HMM) is then trained on the whole sequence of features (Baum-Welsh algorithm), each state of the HMM being associated to a speciﬁc timbre quality. After training and decoding (Viterbi algorithm) the HMM, the signal is assigned a sequence of timbre features according to speciﬁc timbre quality distributions for each possible structural segment. The minimal duration D of expected structural segments can be tuned. The segmentation is then computed by clustering timbre quality histograms. A series of histograms are created using a sliding window and are then grouped into S clusters with an adapted soft K-means

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algorithm. Each of these clusters will correspond to a speciﬁc type of segment in the analyzed signal. The reference histograms describing the timbre distribution for each segment are updated during clustering in an iterative way. The ﬁnal segmentation is obtained from the ﬁnal cluster assignments. Automatic Timbre Recognition. Once the signal has been divided into segments assumed to be homogeneous in timbre, the latter are processed with the automatic timbre recognition technique described in section 3.2 (see Figure 1(b)). This yields intermediate classiﬁcation decisions deﬁned on a short-term basis (depending on the latency L used in the ATR model). Post-processing. Segment-level classiﬁcations are then obtained by choosing the class that appears most frequently amongst the short-term classiﬁcation decisions made within the segments. Software Implementation. The automatic structural segmenter Vamp plugin [43] [35] was run from the terminal using the batch tool for feature extraction Sonic Annotator [11]. Each of the retrieved segments were then processed with the automatic timbre recognition Vamp plugin [7] previously trained for speech and music classes using a Python script. The segment-level classiﬁcation decisions were also computed using a Python script.

4

Experiments

Several experiments were conducted to evaluate and compare the performances of the speech/music discrimination ATR and ASS/ATR methods respectively presented in sections 3.2 and 3.3. In this section, we ﬁrst describe the experimental protocols, and the training and testing databases. The evaluation measures computed to assess the class identiﬁcation and boundary accuracy of the systems are then speciﬁed. 4.1

Protocols

Influence of the Training Class Taxonomy. In a ﬁrst set of experiments, we evaluated the precision of the ATR model according to the taxonomy used to represent speech and music content in the training data. The classes associated to the two taxonomic levels schematized in Figure 5 were tested to train the ATR model. The ﬁrst level correspond to a coarse division of the audio content into two classes: speech and music. Given that common spectral diﬀerences may be observed between male and female speech signals due to vocal tract morphology changes, and that musical genres are often associated with diﬀerent sound textures or timbres due to changes of instrumentation, we sought to establish whether there was any beneﬁt to be gained by training the LSF/K-means algorithm on a wider, more speciﬁc set of classes. Five classes were chosen: two to represent speech (male speech and female speech), and three to represent music according to the genre (classical, jazz, and rock & pop). The classiﬁcations obtained using the algorithm trained on the second, wider, set of classes are later

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Level I

Level II

speech

speech m

speech f

music

classical

jazz

rock & pop

Fig. 5. Taxonomy used to train the automatic timbre recognition model in the speech/music discrimination task. The ﬁrst taxonomic level is associated to a training stage with two classes: speech and music. The second taxonomic level is associated to a training stage with ﬁve classes: male speech (speech m), female speech (speech f), classical, jazz, and rock & pop music.

mapped back down to either speech or music in order to be able to evaluate their correlation with ground truth data and also so that we can compare the two methods. To make a fair comparison between the two methods, we kept the same training excerpts in both cases and hence kept constant the content duration for the speech and music classes. Comparison Between ATR and ASS/ATR. A second set of experiments was performed to compare the performances of the ATR and ASS/ATR methods. In these experiments the automatic timbre recognition model was trained with ﬁve classes (second taxonomic level), case which lead to the best performances (see section 5.1). The number of clusters used in the K-means classiﬁer of the ATR method was kept constant and tuned to a value that yielded good results in a musical instrument recognition task (K=32) [16]. In order to ﬁnd the best conﬁguration, the number of line spectral frequencies was varied in the feature extraction stage (LSF={8;16;24;32}) since the number of formants in speech and music spectra is not known a priori and is not expected to be the same. If voice is typically associated with four or ﬁve formants (hence 8 or 10 LSFs), this number may be higher in music due to the superpositions of various instruments’ sounds. The parameter of the automatic structural segmentation algorithm setting the minimal duration of retrieved segments was set to 2 s since shorter events are not expected, and longer durations could decrease the boundary retrieval accuracy. Since the content of audio podcasts can be broad (see next section 4.2), the maximal number of segments S of the ASS was varied between 7 and 12. Classiﬁcation tests were also performed with the algorithm proposed by Ramona and Richard in [44] which provides a good benchmark of the state-of-the-art performance for speech/music discriminators. This algorithm which relies on a feature-based approach with a support vector machine classiﬁer (previously described in section 2) is however computationnaly expensive since a large collection of about 600 features of various types (temporal, spectral, cepstral, and perceptual) is computed in the training stage.

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Database

The training data used in the automatic timbre recognition system consisted of a number of audio clips extracted from a wide variety of radio podcasts from BBC 6 Music (mostly pop) and BBC Radio 3 (mostly classical and jazz) emissions. The clips were manually auditioned and then, classiﬁed as either speech or music when the ATR model was trained with two classes, or as male speech, female speech, classical music, jazz music, and rock & pop music when the ATR model was trained with ﬁve classes. These manual classiﬁcations constituted the ground truth annotations further used in the algorithm evaluations. All speech was english language, and the training audio clips, whose durations are shown in Table 1, gathered approximately 30 min. of speech, and 15 min. of music. For testing purposes, four podcasts diﬀerent from the ones used for training (hence containing diﬀerent speakers and music excerpts) were manually annotated using terms from the following vocabulary: speech, multi-voice speech, music, silence, jingle, efx (eﬀects), tone, tones, beats. Mixtures of these terms were also employed (e.g. “speech + music”, to represent speech with background music). The music class included cases where a singing voice was predominant (opera and choral music). More detailed descriptions of the podcast material used for testing are given in Tables 2 and 3. 4.3

Evaluation Measures

We evaluated the speech/music discrimination methods with regards to two aspects: (i) their ability to correctly identify the considered classes (semantic level), and (ii) their ability to correctly retrieve the boundary locations between classes (temporal level). Relative Correct Overlap. Several evaluation measures have been proposed to assess the performances of audio content classiﬁers depending on the time scale considered to perform the comparison between the machine estimated classiﬁcations and the ground-truth annotations used as reference [28]. The accuracy of the models can indeed be measured on a frame-level basis by resampling the ground-truth annotations at the frequency used to make the estimations, or on Table 1. Audio training data durations. Durations are expressed in the following format: HH:MM:SS (hours:mn:s). Training class Total duration of audio clips (HH:MM:SS) Speech 00:27:46 Two class training Music 00:14:30 Male speech 00:19:48 Female speech 00:07:58 Five class training Total speech 00:27:46 Classical 00:03:50 Jazz 00:07:00 Rock & Pop 00:03:40 Total music 00:14:30

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Table 3. Audio testing data durations. Durations are expressed in the following format: HH:MM:SS (hours:mn:s). Podcast Total duration Speech duration Music duration 1 00:51:43 00:38:52 00:07:47 2 00:53:32 00:32:02 00:18:46 3 00:45:00 00:18:09 00:05:40 4 00:47:08 00:06:50 00:32:31 Total 03:17:23 01:35:53 01:04:43

a segment-level basis by considering the relative proportion of correctly identiﬁed segments. We applied the latter segment-based method by computing the relative correct overlap (RCO) measure used to evaluate algorithms in the music information retrieval evaluation exchange (MIREX) competition [39]. The relative correct overlap is deﬁned as the cumulated duration of segments where the correct class has been identiﬁed normalized by the total duration of the annotated segments: RCO =

|{estimated segments} ∩ {annotated segments}| |{annotated segments}|

(1)

where {.} denotes a set of segments, and —.— their duration. When comparing the machine estimated segments with the manually annotated ones, any sections not labelled as speech (male or female), multi-voice speech, or music (classical, jazz, rock & pop) were disregarded due to their ambiguity (e.g. jingle). The durations of these disregarded parts are stated in the results section. Boundary Retrieval F-measure. In order to assess the precision with which the algorithms are able to detect the time location of transitions from one class to another (i.e. start/end of speech and music sections), we computed the boundary retrieval F-measure proposed in [35] and used in MIREX to evaluate the temporal accuracy of automatic structural segmentation methods [41]. The boundary retrieval F-measure, denoted F in the following, is deﬁned as the harmonic mean 2P R between the boundary retrieval precision P and recall R (F = ). The P +R boundary retrieval precision and recall are obtained by counting the numbers of

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correctly detected boundaries (true positives tp), false detections (false positives f p), and missed detections (false negatives f n) as follows: tp P = (2) tp + f p tp R= (3) tp + f n Hence, the precision and the recall can be viewed as measures of exactness and completeness, respectively. As in [35] and [41], the number of true positives were determined using a tolerance window of duration ΔT = 3 s: a retrieved boundary is considered to be a “hit” (correct) if its time position l lies within ΔT ΔT the range l − ≤ l ≤ l+ . This method to compute the F-measure is also 2 2 used in onset detector evaluation [18] (the tolerance window in the latter case being much shorter). Before comparing the manually and the machine estimated boundaries, a post-processing was performed on the ground-truth annotations in order to remove the internal boundaries between two or more successive segments whose type was discarded in the classiﬁcation process (e.g. the boundary between a jingle and a sound eﬀect section).

5

Results and Discussion

In this section, we present and discuss the results obtained for the two sets of experiments described in section 4.1. In both sets of experiments, all audio training clips were extracted from 128 kbps, 44.1 kHz, 16 bit stereo mp3 ﬁles (mixed down to mono) and the podcasts used in the testing stage were full duration mp3 ﬁles of the same format. 5.1

Influence of the Training Class Taxonomies in the ATR Model

Analysis Parameters. The LSF/K-means algorithm in the automatic timbre recognition model was performed with a window of length 1024 samples (approximately 20 ms), and hop length of 256 samples (approximately 5 ms). A combination of 24 line spectral frequencies and 32 codevectors was used as in [7]. During testing, the intermediate classiﬁcations were made with a latency of 1 s. The post-processing of the machine estimated annotations was performed by resampling the data with a sampling period of 0.1 s, and processing them with a median ﬁlter using a 20 s long window. Performances. Table 4 shows the relative correct overlap (RCO) performances of the speech/music discriminator based on automatic timbre recognition for each of the four podcasts used in the test set, as well as the overall results (podcasts 1 to 4 combined). The sections that were neither speech nor music and were disregarded lasted in overall 36 min. 50 s. The RCO measures are given both when the model was trained on only two classes (music and speech), and when it was trained on ﬁve classes (male speech, female speech, classical,

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Table 4. Inﬂuence of the training class taxonomy on the performances of the automatic timbre recognition model assessed at the semantic level with the relative correct overlap (RCO) measure ATR model - RCO measure (%) 1 (Rock & pop) 2 (Classical) 3 (Classical) 4 (Rock & pop) Overall

Speech Music Two class Five class Two class Five class 90.5 91.9 93.7 94.5 91.8 93.0 97.8 99.4 88.3 91.0 76.1 82.7 48.7 63.6 99.8 99.9 85.2 89.2 96.6 97.8

jazz, and rock & pop). We see from that table that training the ATR model on ﬁve classes instead of two improved classiﬁcation performances in all cases, but most notably for the speech classiﬁcations of podcast number 4 (an increase of 14.9% from 48.7% to 63.6%) and for the music classiﬁcations of podcasts number 3 (up from 76.1% to 82.7%, an increase of 6.6%). In all other cases, the increase is more modest; being between 0.1% and 2.7%. The combined results show an increased RCO of 4% for speech and 1.2% for music when trained on ﬁve classes instead of two. 5.2

Comparison between ATR and ASS/ATR

Analysis Parameters. The automatic timbre model was trained with ﬁve classes since this conﬁguration gave the best RCO performances. Regarding the ATR method, the short-term analysis was performed with a window of 1024 samples, a hop size of 256 samples, and K = 32 codevectors, as in the ﬁrst set of experiments. However, in this set of experiments the number of line spectral frequencies LSF were varied between 8 and 32 by steps of 8, and the duration of the median ﬁltering windows were tuned accordingly based on experimenting. The automatic structural segmenter Vamp plugin was used with the default window and hop sizes (26460 samples, i.e. 0.6 s, and 8820 samples, i.e. 0.2 s, respectively), parameters deﬁned based on typical beat-length in music [35]. Five diﬀerent number of segments S were tested (S = {5;7;8;10;12}). The best relative correct overlap and boundary retrieval performances were obtained with S = 8 and S = 7, respectively. Relative Correct Overlap Performances. Table 5 presents a comparison of the relative correct overlap (RCO) results obtained for the proposed speech/music discriminators based on automatic timbre recognition ATR, and on automatic structural segmentation and timbre recognition ASS/ATR. The performances obtained with the algorithm from Ramona et Richard [44] are also reported. The ATR and ASS/ATR methods obtain very similar relative correct overlaps. For both methods, the best conﬁguration is obtained with the lowest number of features (LSF = 8) yielding high average RCOs, 94.4% for ATR, and 94.5% for ASS/ATR. The algorithm from [44] obtain a slightly higher average RCO (increase of approximately 3%) but may require more computations than the

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Table 5. Comparison of the relative correct overlap performances for the ATR and ASS/ATR methods, as well as the SVM-based algorithm from [44]. For each method, the best average result (combining speech and music) is indicated in bold RCO (%) Podcast Class speech 1 music speech 2 music speech 3 music speech 4 music speech Overall music Average

ATR LSF number 8 16 24 32 94.8 94.8 94.7 94.3 94.9 92.4 90.8 92.8 94.2 95.4 92.9 92.8 98.8 98.7 98.8 98.1 96.7 96.9 93.5 92.0 96.1 79.0 76.8 77.4 55.3 51.9 56.4 58.9 99.5 99.5 99.9 99.5 90.3 90.0 89.5 89.5 98.5 96.1 96.1 96.3 94.4 93.1 92.8 92.9

ASS/ATR SVM [44] LSF number n/a 8 16 24 32 n/a 96.9 95.8 96.9 96.9 97.5 84.3 82.5 82.3 86.3 94.1 96.3 96.3 96.3 96.1 97.6 97.1 94.2 96.5 96.9 99.9 96.4 95.3 93.6 93.5 97.2 92.3 85.8 77.5 83.5 96.9 61.8 48.5 60.2 65.6 88.6 99.7 100 100 100 99.5 92.8 89.4 92.0 92.8 96.8 96.2 94.4 94.3 95.8 98.8 94.5 91.9 93.2 94.3 97.3

ATR method (the computation time has not been measured in these experiments). The lower performances obtained by the three compared methods for the speech class of the fourth podcast is to be nuanced by the very short proportion of spoken excerpts within this podcast (see Table 3), which hence does not aﬀect much the overall results. The good performances obtained with a low dimensional LSF vector can be explained by the fact that the voice has a limited number of formants that are therefore well characterized by a small number of line spectral frequencies (LSF = 8 corresponds to the characterization of 4 formants). Improving the recognition accuracy for the speech class diminishes the confusions made with the music class, which explains the concurrent increase of RCO for the music class when LSF = 8. When considering the class identiﬁcation accuracy, the ATR method conducted with a low number of LSF hence appears interesting since it is not computationally expensive relatively to the performances of modern CPUs (linear predictive ﬁlter determination, computation of 8 LSFs, K-means clustering and distance computation). For the feature vectors of higher dimensions, the higher-order LSFs may contain information associated with the noise in the case of the voice which would explain the drop of overall performances obtained with LSF = 16 and LSF = 24. However the RCOs obtained when LSF = 32 are very close to that obtained when LSF = 8. In this case, the higher number of LSF may be adapted to capture the more complex formant structures of music. Boundary Retrieval Performances. The boundary retrieval performance measures (F-measure, precision P, and recall R) obtained for the ATR, ASS/ATR, and SVM-based method from [44] are reported in Table 6. As opposed to the relative correct overlap evaluation where the ATR and ASS/ATR methods obtained similar performances, the ASS/ATR method clearly

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Table 6. Comparison of the boundary retrieval measures (F-measure, precision P, and recall R) for the ATR and ASS/ATR methods, as well as the SVM-based algorithm from [44]. For each method, the best overall result is indicated in bold. Boundary retrieval

ATR LSF number Podcast Measures (%) 8 16 24 P 40.0 45.7 31.0 1 R 21.3 34.0 19.1 F 27.8 39.0 23.7 P 61.5 69.0 74.1 2 R 37.5 31.3 31.3 F 46.6 43.0 44.0 P 69.2 54.5 56.7 3 R 24.3 32.4 23.0 F 36.0 40.7 32.7 P 11.7 12.3 21.7 4 R 21.9 21.9 15.6 F 15.2 15.7 18.2 P 23.3 40.6 46.8 Overall R 27.2 30.9 23.5 F 32.2 35.1 31.3

ASS/ATR SVM [44] LSF number n/a 8 16 24 32 n/a 43.6 36.0 37.2 34.1 36.0 36.2 38.3 34.0 31.9 57.4 39.5 37.1 35.6 33.0 44.3 72.7 35.3 84.6 71.9 58.2 37.5 37.5 34.4 35.9 60.9 49.5 36.4 48.9 47.9 59.5 75.0 68.0 60.4 64.0 67.3 44.6 45.9 43.2 43.2 50.0 55.9 54.8 50.4 51.6 57.4 56.7 57.1 57.7 48.5 28.6 53.1 50.0 46.9 50.0 50.0 54.8 53.3 51.7 49.2 57.4 62.3 46.9 57.4 54.1 47.0 41.9 42.4 39.2 39.6 54.8 50.1 44.6 46.6 45.7 50.6

outclassed the ATR method regarding the boundary retrieval accuracy. The best overall F-measure of the ASS/ATR method (50.1% with LSF = 8) is approximately 15% higher than the one obtained with the ATR method (35.1% for LSF = 16). This shows the beneﬁt of using the automatic structural segmenter prior to the timbre recognition stage to locate the transitions between the speech and music sections. As in the previous set of experiments, the best conﬁguration is obtained with a small amount of LSF features (ASS/ATR method with LSF = 8) which stems from the fact the boundary positions are a consequence of the classiﬁcation decisions. For all the tested podcasts, the ASS/ATR method yields a better precision than the SVM-based algorithm. The most notable diﬀerence happens for the second podcast where the precision of the ASS/ATR method (72.7%) is approximately 14% higher than the one obtained with the SVM-based algorithm (58.2%). The resulting increase in overall precision achieved with the ASS/ATR method (62.3%) compared with the SVM-based method (47.0%) is of approximately 15%. The SVM-based method however obtains a better overall boundary recall measure (54.8%) than the ASS/ATR method (42.4%), inducing the boundary F-measures of both methods to be very close (50.6% and 50.1%, respectively).

6

Summary and Conclusions

We proposed two methods for speech/music discrimination based on timbre models and machine learning techniques and compared their performances with

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audio podcasts. The ﬁrst method (ATR) relies on automatic timbre recognition (LSF/K-means) and median ﬁltering. The second method (ASS/ATR) performs an automatic structural segmentation (MFCC, RMS / HMM, K-means) before applying the timbre recognition system. The algorithms were tested with more than 2,5 hours of speech and music content extracted from popular and classical music podcasts from the BBC. Some of the music tracks contained a predominant singing voice which can be a source of confusion with the spoken voice. The algorithms were evaluated both at the semantic level to measure the quality of the retrieved segment-type labels (classiﬁcation relative correct overlap), and at the temporal level to measure the accuracy of the retrieved boundaries between sections (boundary retrieval F-measure). Both methods obtained similar and relatively high segment-type labeling performances. The ASS/ATR method lead to a RCO of 92.8% for speech, and 96.2% for music, yielding an average performance of 94.5%. The boundary retrieval performances were higher for the ASS/ATR method (F-measure = 50.1%) showing the beneﬁt to use a structural segmentation technique to locate transitions between diﬀerent timbral qualities. The results were compared against the SVM-based algorithm proposed in [44] which provides a good benchmark of the state-of-the-art’s speech/music discriminators. The performances obtained by the ASS/ATR method were approximately 3% lower than those obtained with the SVM-based method for the segment-type labeling evaluation, but lead to better boundary retrieval precisions (approximately 15% higher). The boundary retrieval scores were clearly lower for the three compared methods, relatively to the segment-type labeling performances which were fairly high, up to 100% of correct identiﬁcations in some cases. Future works will be dedicated to reﬁne the accuracy of the sections’ boundaries either by performing a new analysis of the feature variations locally around the retrieved boundaries, or by including descriptors complementary to the timbre ones, by using e.g. the rhythmic information such as tempo whose ﬂuctuations around speech/music transitions may give complementary clues to accurately detect them. The discrimination of intricate mixtures of music, speech, and sometimes strong postproduction sound eﬀects (e.g. the case of jingles) will also be investigated. Acknowledgments. This work was partly funded by the Musicology for the Masses (M4M) project (EPSRC grant EP/I001832/1, http://www.elec.qmul. ac.uk/digitalmusic/m4m/), the Online Music Recognition and Searching 2 (OMRAS2) project (EPSRC grant EP/E017614/1, http://www.omras2.org/), and a studentship (EPSRC grant EP/505054/1). The authors wish to thank Matthew Davies from the Centre for Digital Music for sharing his F-measure computation Matlab toolbox, as well as Gy¨ orgy Fazekas for fruitful discussions on the structural segmenter. Many thanks to Mathieu Ramona from the Institut de Recherche et Coordination Acoustique Musique (IRCAM) for sending us the results obtained with his speech/music segmentation algorithm.

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Computer Music Cloud Jes´ us L. Alvaro1 and Beatriz Barros2 1

2

Computer Music Lab, Madrid, Spain [email protected] http://cml.fauno.org Departamento de Lenguajes y Ciencias de la Computaci´ on Universidad de M´ alaga, Spain [email protected]

Abstract. The present paper puts forward a proposal for computer music (CM) composition system on the Web. Setting oﬀ from the CM composition paradigm used so far and on the basis of the current computer technology shift into cloud computing, a new paradigm is open for the CM composition domain. An experience of computer music cloud (CMC) is described: the whole music system is split into several web services sharing an unique music representation. MusicJSON is proposed as the interchangeable music data format based on the solid and ﬂexible EvMusic representation. A web browser-based graphic environment is developed as the user interface for the Computer Music Cloud as music web applications. Keywords: Music Representation, Cloud Computing, Computer Music, Knowledge Representation, Music Composition, UML, Distributed Data, Distributed Computing, Creativity, AI.

1

Computer Aided Composition

Computers oﬀer composers multiple advantages, from score notation to sound synthesis, algorithmic composition and music artiﬁcial intelligence (MAI) experimentation. Fig. 1 shows the basic structure of a generalized CM composition system. In this ﬁgure, music composition is intentionally divided into two functional processes: a composer-level computation and a performance-level composition [8]. Music computation systems, like algorithmic music programs, are used to produce music materials in an intermediate format, usually a standard midi ﬁle (SMF). These composition materials are combined and post-produced for performance by means of a music application which ﬁnally produces a score or sound render of the composition. In some composition systems the intermediate format is not so evident, because the same application carries out both functions, but in terms of music representation, some symbols representing music entities are used for computation. This internal music representation determines the creative capabilities of the system. S. Ystad et al. (Eds.): CMMR 2010, LNCS 6684, pp. 163–175, 2011. c Springer-Verlag Berlin Heidelberg 2011

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Fig. 1. Basic Structure of a CM Composition System

There are many diﬀerent approaches for a CM composition system, as well as multiple languages and music representations. As shown in Fig. 3, our CM system has substantially evolved during the last 12 years [1]. Apart from musical and creative requirements, these changes have progressively accommodated technology changes and turned the system into a distributed computing approach. Computer-assisted music composition and platforms have evolved for 50 years. Mainframes were used at the beginning and personal computers (PCs) arrived in the 1980s, bringing computation to the general public. With network development, Internet has gained more and more importance within the present-day Information Technology (IT) and dominates the current situation, making irrelevant the geographical location of IT resources. This paper is aimed at presenting a proposal for computer music (CM) composition on the web. Starting from the CM paradigm used so far, and on the basis of the current computer technology shift into cloud computing, a new paradigm is open for the CM domain. The paper is organized as follows: the next section describes the concept of cloud computing, thus locating the work within the ﬁeld of ITs. Then, section 3 introduces the EV representation, which is the basis of the proposed composition paradigm, explained in section 4. Next, an example is sketched in section 5, while section 6 presents the MusicJSON music format: the interchangeable music data format based on the solid and ﬂexible EvMusic representation. The paper ends with some conclusions and ideas for future research.

2

Cloud Computing

IT continues evolving. Cloud Computing, a new term deﬁned in various diﬀerent ways [8], involves a new paradigm in which computer infrastructure and software are provided as a service [5]. These services themselves have been referred to as Software as a Service (SaaS ). Google Apps is a clear example of SaaS [10]. Computation infrastructure is also oﬀered as a service (IaaS ), thus enabling the user to run the customer software. Several providers currently oﬀer resizable compute capacity as a Public Cloud, such as the Amazon Elastic Compute Cloud (EC2) [4] and the Google AppEngine [9]. This situation oﬀers new possibilities for both software developers and users. For instance, this paper was written and revised in GoogleDocs [11], a Google

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web service oﬀering word processing capabilities online. The information is no longer stored in local hard discs but in Google servers. The only software users need is a standard web browser. Could this computing in the cloud approach be useful for music composition? What can it oﬀer? What type of services does a music composition cloud consist of? What music representation should they share? What data exchange format should be used?

3

EvMusic Representation

The ﬁrst step when planning a composition system should be choosing a proper music representation. The chosen representation will set the frontiers of the system’s capabilities. As a result, our CM research developed a solid and versatile representation for music composition. EvMetamodel [3] was used to model the music knowledge representation behind EvMusic. A previous, deep analysis of music knowledge was carried out to assure the representation meets music composition requirements. This multilevel representation is not only compatible with traditional notation but also capable of representing highly abstract music elements. It can also represent symbolic pitch entities [1] from both music theory and algorithmic composition, keeping the door open to the representation of the music elements of higher symbolic level conceived by the composer’s creativity. It is based on real composition experience and was designed to support CM Composition, including experiences in musical artiﬁcial intelligence (MAI). Current music representation is described in a platform-independent UML format [15]. Therefore, it is not conﬁned to its original LISP system, but can be used in any system or language: a valuable feature when approaching a cloud system.

Fig. 2. UML class diagram of the EvMetamodel

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Fig. 2 is an UML class diagram for the representation core of the EvMetamodel, the base representation for time dimension. The three main classes are shown: event, parameter and dynamic object. High level music elements are represented as subclasses of metaevent, the interface which provides the develop functionality. The special dynamic object changes is also shown. This is a very useful option for the graphic edition of parameters, since it represents a dynamic object as a sequence of parameter-change events which can be easily moved in time.

4

Composing in the Cloud: A New Paradigm

Our CM system underwent several changes since the beginning, back to 1997. Fig. 3 shows the evolutions undergone by formats, platforms and technologies toward the current proposal. This ﬁgure follows the same horizontal scheme shown in Figure 1. The ﬁrst column indicates the user interface for music input, and the second shows the music computation system and its evolution over recent years, while the music intermediate format is reported in the central column. Post-production utilities and their ﬁnal results are shown in last two columns, respectively. The model in Fig. 1 clearly shows the evolution undergone by the system. First, a process of service diversiﬁcation and specialisation, mainly at the postproduction stage; second, as a trend in user input, CM is demanding graphic environments. Finally, technologies and formats undergo multiple changes. The reason behind most of these changes can be found in external technology advances and the need to accommodate to these new situations. At times the

Fig. 3. Evolution of our CM Composition System

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needed tool or library was not available at that time. At others the available tool was suitable at that moment, but oﬀered no long-term availability. As stated above, the recent shift of IT into cloud computing brings new opportunities for evolution. In CM, system development can beneﬁt from computing distribution and specialization. Splitting the system into several specialized services prevents the limitations involved by a single programming language or platform. Therefore, individual music services can be developed and evolved independently from the others. Each component service can be implemented in the most appropriate platform for its particular task, regardless of the rest of services, without being conditioned by the technologies necessary for the implementation of other services. In the previous paradigm, all services were performed by one only system, and the selection of technologies to complete a particular task aﬀected or even conditioned the implementation of other tasks. This frees the system design, thus making it more platform-independent. In addition, widely-available tools can be used for speciﬁc tasks, thus beneﬁtting from tool development in other areas such as database storage and web application design. 4.1

Computer Music Cloud (CMC)

Fig. 4 shows a Computer Music Cloud (CMC) for composition as an evolution of the scheme in Fig. 3. The system is distributed across specialized online services. The user interface is now a web application running in a standard browser. A storage service is used as an edition memory. An intelligent-dedicated service is allocated for music calculation and development. Output formats such as MIDI, graphic score and sound ﬁle are rendered by independent services exclusively devoted to this task. The web application includes user sessions to allow multiple users utilizing the system. Both public and user libraries are also provided for music objects. Intermediary music elements can be stored in the library and also serialized into a MusicJSON format ﬁle, as described below. An advantage involved by this approach is the availability of a single service for diﬀerent CMC systems. Therefore, the design of new music systems is facilitated by the joint work of diﬀerent services controlled by a web application. The key factor for successful integration lies in the use of a well-deﬁned suitable music representation for music data exchange. 4.2

Music Web Services

Under Cloud Computing usual notation, each of the services can be considered as a Music-computing as a Service (MaaS) component. In a simple form, they are servers receiving a request and performing a task. At the end of the task, resulting objects are returned to the stream or stored in an exchange database. The access to this database is a valuable feature for services, since it allows the deﬁnition of complex services. They could even be converted into real MAI agents (i.e., intelligent devices which can perceive their environment, make decisions and act inside their environment) [17]. Storing the music composition as a virtual environment in a database allows music services interacting within the composition, thus opening a door toward a MAI system of distributed agents. Music Services of the Cloud are classiﬁed according their function.

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Fig. 4. Basic Structure of a CMC

Input. This group includes the services aimed particularly at incorporating new music elements and translating them from other input formats. Agents. They are those services which are capable of inspecting and modifying music composition, as well as introducing new elements. This type includes human user interfaces, but may also include other intelligent elements taking part in composition introducing decisions, suggestions or modiﬁcations. In our prototype, we have developed a web application acting as a user interface for the edition of music elements. This service is described in the next section. Storage. At this step, only music object instances and relations are stored, but the hypothetical model also includes procedural information. Three music storage services are implemented in the prototype. Main lib stores shared music elements as global deﬁnitions. This content may be seen as some kind of music culture. User-related music objects are stored in the user lib. These include music objects deﬁned by the composer which can be reused in several parts and complete music sections or represent the composer’s style. The editing storage service is provided as temporary storage for the editing session. The piece is progressively composed in the database. The composition environment (i.e., everything related to the piece under composition) is in the database. This is the environment in which several composing agents can integrate and interact by reading and writing on this database-stored score. All three storage services in this experience, were written in Python and clouded with Google AppEngine. Development. The services in this group perform development processes. As explained in [8], development is the process by which higher-abstraction symbolic elements are turned into lower-abstraction elements. High-abstraction symbols

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are implemented as meta-events and represent music objects such as motives, segments and other composing abstractions [3]. In this prototype, the entire EvMusic LISP implementation is provided as a service. Other intelligent services in this group such as constraint solvers, genetic algorithms and others may also be incorporated. Output. These services produce output formats as a response to requests from other services. They work in a two-level scheme. At the ﬁrst level, they render formats for immediate composer feedback such as MIDI playback or .PNG graphic notation from the element currently under edition. Composition products such as the whole score or the rendering of audio are produced at the second level. In this experience, a MIDI service is implemented in Python language and run in Google AppEngine. It returns a SMF for quick audible monitoring. A LISP -written Score Service is also available. It uses FOMUS [16] and LiLypond [14] libraries and runs in an Ubuntu Linux image[18] for the Amazon Cloud. It produces graphic scores from the music data as .PNG and .PDF ﬁles. Included in this output group, the MusicJSON serializer service produces a music exchange ﬁle, as described in the next section.

5

User Interface: A Music Web Application

What technologies are behind those successful widespread web cloud applications such as GoogleDocs? What type of exchange format do they use? JavaScript [13] is a dialect of the standard ECMAScript [6] supported for almost all web browsers. It is the key tool behind these web clients. In this environment, the code for the client is downloaded from a server and then run in the web browser as a dynamic script keeping communication with web services. Thus, the browser window behaves as a user-interface window for the system. 5.1

EvEditor Client

The Extjs library [7] was chosen as a development framework for client side implementation. The web application takes the shape of a desktop with windows archetype (i.e., a well-tested approach and an intuitive interface environment we would like to beneﬁt from, but within a web-browser window). The main objective of its implementation was not only producing a suitable music editor for a music cloud example but also a whole framework for the development of general-purpose object editors under an OOP approach. Once this aim was reached, diﬀerent editors could be subclassed with the customizations required from the object type. Extjs is a powerful JavaScript library including a vast collection of components for user interface creation. Elements are deﬁned in a hierarchic class system, which oﬀers a great solution for our OOP approach. That is, all EvEditor components are subclassed from ExtJS classes. As shown in Fig. 5, EvEditor client is a web application consisting of three layers. The bottom or data layer contains an editor proxy for data under current edition. It duplicates the selected records in the remote storage service. The database in the

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PROXY & DATA

COMPONENT LAYER

DOM

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Fig. 5. Structure of EvEditor Client

remote storage service is synched with the editions an updates the editor writes in its proxy. Several editors can share the same proxy, so all listening editors are updated when the data are modiﬁed in the proxy. The intermediate layer is a symbolic zone for all components. It includes both graphic interface components such as editor windows, container views and robjects: representations for the objects under current edition. Interface components are subclassed from ExtJS components. Every editor is displayed in its own window on the working desktop and optionally contains a contentView displaying its child objects as robjects.

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Fig. 6. Screen Capture of EvEditor

Fig. 6 is a browser-window capture showing the working desktop and some editor windows. The application menu is shown in the lower left-hand corner, including user setting. The central area of the capture shows a diatonic sequence editor based on our TclTk Editor [1]. A list editor and a form-based note editor are also shown. In the third layer or DOM (Document Object Model) [19], all components are rendered as DOM elements (i.e., HTML document elements to be visualized). 5.2

Server Side

The client script code is dynamically provided by the server side of the web application. It is written in Python and can run as a GoogleApp for integration into the cloud. All user-related tasks are managed by this server side. It identiﬁes the user, and manages sessions, proﬁles and environments.

6

MusicJSON Music Format

As explained above, music services can be developed in the selected platform. The only requirement for the service to be integrated into the music composition cloud is that it must use the same music representation. EvMusic representation is proposed for this purpose. This section describes how EvMusic objects are stored in a database and transmitted over the cloud.

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Database Object Storage

Database storage allows several services sharing the same data and collaborating in the composition process. The information stored in a database is organized in tables of records. For storing EvMusic tree structures in a database, they must be previously converted into records. For this purpose, the three main classes of Ev representation are subclassed from a tree node class, as shown in Fig. 2. Thus, every object is identiﬁed by an unique reference and a parent attribute. This allows representing a large tree structure of nested events as a set of records for individual retrieval or update. 6.2

MusicJSON Object Description

Web applications usually use XML and JSON (Java Script Object Notation) for data exchange [12]. Both formats meet the requirements. However, two reasons supported our inclination for JSON: 1) The large tool library available for JSON at the time of this writing, and 2) the fact that JSON is oﬀered as the exchange format for some of the main Internet web services such as Google or Yahoo. The second reason has to do with its great features, such as human readability and dynamic unclosed object support, a very valuable feature inherited from the prototype-based nature of JavaScript. JSON can be used to describe EvMusic objects and to communicate among web music services. MusicJSON [2] is the name given to this use. As a simple example, the code below shows the description for the short music fragment shown in Fig. 7. As it can be seen, the code is self-explanatory. {"pos":0, "objclass": "part","track":1, "events":[ {"pos":0, "objclass": "note","pitch":"d4","dur":0.5, "art":"stacatto"}, {"pos":0.5, "objclass": "note","pitch":"d4","dur":0.5, "art":"stacatto"}, {"pos":1, "objclass": "pitch":"g4","dur":0.75, "dyn":"mf","legato":"start"}, {"pos":1.75, "objclass": "pitch":"f#4","dur":0.25 "legato":"end"}, {"pos":2, "objclass": "note","pitch":"g4","dur":0.5, "art":"stacatto"} {"pos":2.5, "objclass": "note","pitch":"a4","dur":0.5, "art":"stacatto"} {"pos":3, "objclass":"nchord","dur":1,"pitches":[ {"objclass": "spitch","pitch": "d4"}, {"objclass": "spitch","pitch": "b4"}] } ] }

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Fig. 7. Score notation of the example code

6.3

MusicJSON File

Every EvMusic object, from single notes to complex structures, can be serialized into a MusicJSON text and subsequently transmitted through the Web. In addition, MusicJSON can be used as an intermediate format for local storage of compositions. The next listing code shows a draft example of the proposed description of an EvMusic ﬁle. {"objclass":"evmusicfile","ver":"0911","content": {"lib":{ "instruments":"http://evmusic.fauno.org/lib/main/instruments", "pcstypes": "http://evmusic.fauno.org/lib/main/pcstypes", "mypcs": "http://evmusic.fauno.org/lib/jesus/pcstypes", "mymotives": "http://evmusic.fauno.org/lib/jesus/motives" }, "def":{ "ma": {"objclass":"motive", "symbol":[ 0,7, 5,4,2,0 ], "slength": "+-+- +-++ +---"}, "flamenco": {"objclass":"pcstype", "pcs":[ 0,5,7,13 ],}, }, "orc":{ "flauta": {"objclass":"instrument", "value":"x.lib.instruments.flute", "role":"r1"} "cello": {"objclass":"instrument", "value":"x.lib.instruments.cello", "role":"r2"} }, "score":[ {"pos": 0,"objclass":"section", "pars":[ "tempo":120,"dyn":"mf","meter":"4/4", ... ], "events":[ {"pos":0, "objclass": "part","track":1,"role":"i1", "events":[ ... ] ... ]}, {"pos": 60,"objclass":"section","ref":"s2", ... },],}}}

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The code shows four sections in the content. Library is an array of libraries to be loaded with object deﬁnitions. Both main and user libraries can be addressed. The following section includes local deﬁnitions of objects. As an example, a motive and a chord type are deﬁned. Next section establishes instrumentation assignments by means of the arrangement object role. Last section is the score itself, where all events are placed in a tree structure using parts. Using MusicJSON as the intermediary communication format enables us to connect several music services conforming a cloud composition system.

7

Conclusion

The present paper puts forward an experience of music composition under a distributed computation approach as a viable solution for Computer Music Composition in the Cloud. The system is split into several music services hosted in common IaaS providers such as Google or Amazon. Diﬀerent music systems can be built by joint operation of some of these music services in the cloud. In order to cooperate and deal with music objects, each service in the music cloud must understand the same music knowledge. The music knowledge representation they must share must be therefore standardized. EvMusic representation is proposed for this, since it is a solid multilevel representation successfully tested in real CM compositions in recent years. Furthermore, MusicJSON is proposed as an exchange data format between services. Example descriptions of music elements, as well as a ﬁle format for local saving of a musical composition, are given. A graphic environment is also proposed for the creation of user interfaces for object editing as a web application. As an example, the EvEditor application is described. This CMC approach opens multiple possibilities for derivative work. New music creation interfaces can be developed as web applications beneﬁting from the upcoming web technologies such as the promising HTML5 standard [20]. The described music in the cloud, together with EvMusic representation, provides a ground environment for MAI research, where especialised agents can cooperate in a music composition environment sharing the same music representation.

References 1. Alvaro, J.L. : Symbolic Pitch: Composition Experiments in Music Representation. Research Report, http://cml.fauno.org/symbolicpitch.html (retrieved December 10, 2010) (last viewed February 2011) 2. Alvaro, J.L., Barros, B.: MusicJSON: A Representation for the Computer Music Cloud. In: Proceedings of the 7th Sound and Music Computer Conference, Barcelona (2010) 3. Alvaro, J.L., Miranda, E.R., Barros, B.: Music knowledge analysis: Towards an eﬃcient representation for composition. In: Mar´ın, R., Onaind´ıa, E., Bugar´ın, A., Santos, J. (eds.) CAEPIA 2005. LNCS (LNAI), vol. 4177, pp. 331–341. Springer, Heidelberg (2006)

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4. Amazon Elastic Computing, http://aws.amazon.com/ec2/ (retrieved February 1, 2010) (last viewed February 2011) 5. Armbrust, M., Fox, A., Griﬃth, R., Joseph, A. D., Katz, R. H., Konwinski, A., Lee, G., Patterson, D. A., Rabkin, A., Stoica, I., Zaharia, M.: Above the Clouds: A Berkeley View of Cloud Computing White Paper, http://www.eecs.berkeley. edu/Pubs/TechRpts/2009/EECS-2009-28.pdf (retrieved February 1, 2010) (last viewed February 2011) 6. ECMAScript Language Speciﬁcation, http://www.ecma-international.org/ publications/standards/Ecma-262.htm (retrieved February 1, 2010) (last viewed February 2011) 7. ExtJS Library, http://www.extjs.com/ (retrieved February 1, 2010) (last viewed February 2011) 8. Geelan, J.: Twenty Experts Deﬁne Cloud Computing. Cloud Computing Journal (2008), http://cloudcomputing.sys-con.com/node/612375/print (retrieved February 1, 2010) (last viewed February 2011) 9. Google AppEngine, http://code.google.com/appengine/ (retrieved February 1, 2010) (last viewed February 2011) 10. Google Apps, http://www.google.com/apps/ (retrieved February 1, 2010) (last viewed February 2011) 11. Google Docs, http://docs.google.com/ (retrieved February 1, 2010 (last viewed February 2011) 12. Introducing JSON, http://www.json.org/ (retrieved February 1, 2010) (last viewed February 2011) 13. JavaScript, http://en.wikipedia.org/wiki/JavaScript (retrieved February 1, 2010) (last viewed February 2011) 14. Nienhuys, H.-W., Nieuwenhuizen J.: GNU Lilypond, http://www.lilypond.org (rertrieved February 1, 2010) (last viewed February 2011) 15. OMG: Uniﬁed Modeling Language: Superstructure. Version 2.1.1(2007), http:// www.omg.org/uml (retrieved February 1, 2010) (last viewed February 2011) 16. Psenicka, D.: FOMUS, a Music Notation Package for Computer Music Composers, http://fomus.sourceforge.net/doc.html/index.html (retrieved February 1, 2010) (last viewed, February 2011) 17. Russell, S.J., Norvig, P.: Intelligent Agents. In: Artiﬁcial Intelligence: A Modern Approach, ch. 2. Prentice-Hall, Englewood Cliﬀs (2002) 18. Ubuntu Server on Amazon EC2, http://www.ubuntu.com/cloud/public (retrieved February 1, 2010) (last viewed, February 2011) 19. Wood, L.: Programming the Web: The W3C DOM Speciﬁcation. IEEE Internet Computing 3(1), 48–54 (1999) 20. W3C: HTML5 A vocabulary and associated APIs for HTML and XHTML W3C Editor’s Draft, http://dev.w3.org/html5/spec/ (retrieved February 1, 2010) (last viewed, February 2011)

Abstract Sounds and Their Applications in Audio and Perception Research Adrien Merer1 , Sølvi Ystad1 , Richard Kronland-Martinet1, and Mitsuko Aramaki2,3 1

2 3

CNRS - Laboratoire de M´ecanique et d’Acoustique, 31 ch. Joseph Aiguier, Marseille, France CNRS - Institut de Neurosciences Cognitives de la M´editerran´ee, 31 ch. Joseph Aiguier, Marseille, France Universit´e Aix-Marseille, 38 bd. Charles Livon, Marseille, France {merer,ystad,kronland}@lma.cnrs-mrs.fr [email protected]

Abstract. Recognition of sound sources and events is an important process in sound perception and has been studied in many research domains. Conversely sounds that cannot be recognized are not often studied except by electroacoustic music composers. Besides, considerations on recognition of sources might help to address the problem of stimulus selection and categorization of sounds in the context of perception research. This paper introduces what we call abstract sounds with the existing musical background and shows their relevance for diﬀerent applications. Keywords: abstract sound, stimuli selection, acousmatic.

1

Introduction

How do sounds convey meaning? How can acoustic characteristics that convey the relevant information in sounds be identiﬁed? These questions interest researchers within various research ﬁelds such as cognitive neuroscience, musicology, sound synthesis, soniﬁcation, etc. Recognition of sound sources, identiﬁcation, discrimination and soniﬁcation deal with the problem of linking signal properties and perceived information. In several domains (linguistic, music analysis), this problem is known as “semiotics” [21]. The analysis by synthesis approach [28] has permitted to understand some important features that characterize the sound of vibrating objects or interaction between objects. A similar approach was also adopted in [13] where the authors use vocal imitations in order to study human sound source identiﬁcation with the assumption that vocal imitations are simpliﬁcations of original sounds that still contain relevant information. Recently, there has been an important development in the use of sounds to convey information to a user (of a computer, a car, etc.) within a new research community called auditory display [19] which deals with topics related to sound design, soniﬁcation and augmented reality. In such cases, it is important to use S. Ystad et al. (Eds.): CMMR 2010, LNCS 6684, pp. 176–187, 2011. c Springer-Verlag Berlin Heidelberg 2011

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sounds that are meaningful independently of cultural references taking into account that sounds are presented through speakers concurrently with other audio/visual information. Depending on the research topics, authors focused on diﬀerent sound categories (i.e. speech, environmental sounds, music or calibrated synthesized stimuli). In [18], the author proposed a classiﬁcation of everyday sounds according to physical interactions from which the sound originates. When working within synthesis and/or soniﬁcation domains, the aim is often to reproduce the acoustic properties responsible for the attribution of meaning and thus, sound categories can be considered from the point of view of semiotics i.e. focusing on information that can be gathered in sounds. In this way, we considered a speciﬁc category of sounds that we call “abstract sounds”. This category includes any sound that cannot be associated with an identiﬁable source. It includes environmental sounds that cannot be easily identiﬁed by listeners or that give rise to many diﬀerent interpretations depending on listeners and contexts. It also includes synthesized sounds, and laboratory generated sounds if they are not associated with a clear origin. For instance, alarm or warning sounds cannot be considered as abstract sounds. In practice, recordings with a microphone close to the sound source and some synthesis methods like granular synthesis are especially eﬃcient for creating abstract sounds. Note that in this paper, we mainly consider acoustically complex stimuli since they best meet our needs in the diﬀerent applications (as discussed further). Various labels that refer to abstract sounds can be found in the literature: “confused” sounds [6], “strange” sounds [36], “sounds without meaning” [16]. Conversely, [34] uses the term “source-bonded” and the expression “source bonding” for the “The natural tendency to relate sounds to supposed sources and causes”. Chion introduced “acousmatic sounds” [9] in the context of cinema and audiovisual applications with the following deﬁnition: “sound one hears without seeing their originating cause - an invisible sound source” (for more details see section 2). The most common expression is “abstract sounds” [27,14,26] particularly within the domain of auditory display, when concerning “earcons” [7]. “Abstract” used as an adjective means “based on general ideas and not on any particular real person, thing or situation” and also “existing in thought or as an idea but not having a physical reality”1. For sounds, we can consider another deﬁnition used for art ”not representing people or things in a realistic way”1 . Abstract as a noun is “a short piece of writing containing the main ideas in a document”1 and thus share the ideas of essential attributes which is suitable in the context of semiotics. In [4], authors wrote: “Edworthy and Hellier (2006) suggested that abstract sounds can be interpreted very diﬀerently depending on the many possible meanings that can be linked to them, and in large depending on the surrounding environment and the listener.” In fact, there is a general agreement for the use of the adjective “abstract” applied to sounds that express both ideas of source recognition and diﬀerent possible interpretations. 1

Deﬁnitions from http://dictionary.cambridge.org/

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This paper will ﬁrst present the existing framework for the use of abstract sounds by electroacoustic music composers and researchers. We will then discuss some important aspects that should be considered when conducting listening tests with a special emphasis on the speciﬁcities of abstract sounds. Finally, three practical examples of experiments with abstract sounds in diﬀerent research domains will be presented.

2

The Acousmatic Approach

Even if the term “abstract sounds” was not used in the context of electroacoustic music, it seems that this community was one of the ﬁrst to consider the issue related to the recognition of sound sources and to use such sounds. In 1966, P. Schaeﬀer, who was both a musician and a researcher, wrote the Trait´e des objets musicaux [29], in which he reported more than ten years of research on electroacoustic music. With a multidisciplinary approach, he intended to carry out fundamental music research that included both “Concr`ete”2 and traditional music. One of the ﬁrst concepts he introduced was the so called “acousmatic” listening, related to the experience of listening to a sound without paying attention to the source or the event. The word “acousmatic” is at the origin of many discussions, and is now mainly employed in order to describe a musical trend. Discussions about “acousmatic” listening was kept alive due to a fundamental problem in Concr`ete music. Indeed, for music composers the problem is to create new meaning from sounds that already carry information about their origins. In compositions where sounds are organized according to their intrinsic properties, thanks to the acousmatic approach, information on the origins of sounds is still present and interacts with the composers’ goals. There was an important divergence of points of view between Concr`ete and Elektronische music (see [10] for a complete review), since the Elektronische music composers used only electronically generated sounds and thus avoided the problem of meaning [15]. Both Concr`ete and Elektronische music have developed a research tradition on acoustics and perception, but only Schaeﬀer adopted a scientiﬁc point of view. In [11], the author wrote: “Schaeﬀer’s decision to use recorded sounds was based on his realization that such sounds were often rich in harmonic and dynamic behaviors and thus had the largest potential for his project of musical research”. This work was of importance for electroacoustic musicians, but is almost unknown by researchers in auditory perception, since there is no published translation of his book except for concomitant works [30] and Chion’s Guide des objets musicaux 3 . As reported in [12], translating Schaeﬀer’s writing is extremely diﬃcult since he used neologisms and very speciﬁc 2

3

The term “concrete” is related to a composition method which is based on concrete material i.e recorded or synthesized sounds, in opposition with “abstract” music which is composed in an abstract manner i.e from ideas written on a score, and become “concrete” afterwards. Translation by J.Dack available at http://www.ears.dmu.ac.uk/spip.php? page=articleEars&id_article=3597

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Fig. 1. Schaeﬀer’s typology. Note that some column labels are redundant since the table must be read from center to borders. For instance, the “Non existent evolution” column in the right part of the table corresponds to endless iterations whereas the “Non existent evolution” column in the left part concerns sustained sounds (with no amplitude variations). Translation from [12]

meanings of french words. However, recently has been a growing interest in this book and in particular in the domain of music information retrieval, for the morphological sound description [27,26,5]. Authors indicate that in the case of what they call “abstract” sounds, classical approaches based on sound source recognition are not relevant and thus base their algorithms on Schaeﬀer’s morphology and typology classiﬁcations. Morphology and typology have been introduced as analysis and creation tools for composers as an attempt to construct a music notation that includes electroacoustic music and therefore any sound. The typology classiﬁcation (cf. ﬁgure 1) is based on a characterization of spectral (mass) and dynamical (facture 4 ) “proﬁles” of with respect to their complexity and consists of twenty-eight categories. There are nine central categories of “balanced” sounds for which the variations are neither too rapid and random nor too slow or nonexistent. Those nine categories included three facture proﬁles (sustained, impulsive or iterative) and three mass proﬁles (tonic, complex and varying). On both sides of the “balanced objects” in the table, there are nineteen additional categories for which mass and facture proﬁles are very simple/repetitive or vary a lot. Note that some automatic classiﬁcation methods are available [26]. In [37] the authors proposed an extension of Schaeﬀer’s typology that includes graphical notations. Since the 1950s, electroacoustic music composers have addressed the problem of meaning of sounds and provided an interesting tool for classiﬁcation of sounds with no a priori diﬀerentiation on the type of sound. For sound perception research, a classiﬁcation of sounds according to these categories may be useful 4

As discussed in [12] even if facture is not a common English word, there is no better translation from French.

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since they are suitable for any sound. The next section will detail the use of such classiﬁcation for the design of listening tests.

3

Design of Listening Tests Using Abstract Sounds

The design of listening tests is a fundamental part of sound perception studies and implies considerations of diﬀerent aspects of perception that are closely related to the intended measurements. For instance, it is important to design calibrated stimuli and experimental procedures to control at best the main factors that aﬀect the subjects’ evaluations. We propose to discuss such aspects in the context of abstract sounds. 3.1

Stimuli

It is common to assume that perception diﬀers as a function of sound categories (e.g. speech, environmental sounds, music). Even more, these categories are underlying elements deﬁning a research area. Consequently, it is diﬃcult to determine a general property of human perception based on collected results obtained from diﬀerent studies. For instance, results concerning loudness conducted on elementary synthesized stimuli (sinusoids, noise, etc.) cannot be directly adapted to complex environmental sounds as reported by [31]. Furthermore, listeners’ judgements might diﬀer for sounds belonging to a same category. For instance, in the environmental sound category, [14] have shown speciﬁc categorization strategies for sounds that involve human activity. When there is no hypothesis regarding the signal properties, it is important to gather sounds that present a large variety of acoustic characteristics as discussed in [33]. Schaeﬀer’s typology oﬀers an objective selection tool than can help the experimenter to construct a very general sound corpus representative of most existing sound characteristics by covering all the typology categories. As a comparison, environmental sounds can be classiﬁed only in certain rows of Schaeﬀer’s typology categories (mainly the “balanced” objects). Besides, abstract sounds may constitute a good compromise in terms of acoustic properties between elementary (sinusoids, noise, etc.) and ecological (speech, environmental sounds and music) stimuli. A corpus of abstract sounds can be obtained in diﬀerent ways. Many databases available for audiovisual applications contain such sounds (see [33]). Diﬀerent synthesis techniques (like granular or FM synthesis, etc.) are also eﬃcient to create abstract sounds. In [16] and further works [38,39], the authors presented some techniques to transform any recognizable sound into an abstract sound, preserving several signal characteristics. Conversely, many transformations drastically alter the original (environmental or vocal) sounds when important acoustic attributes are modiﬁed. For instance, [25] has shown that applying high and low-pass ﬁltering inﬂuence the perceived naturalness of speech and music sounds. Since abstract sounds do not convey univocal meaning, it is possible to use them in diﬀerent ways according to the aim of the experience. For instance, a same sound corpus can be evaluated in diﬀerent contexts (by drawing the listener’s

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attention to certain evocations) in order to study speciﬁc aspects of the information conveyed by the sounds. In particular, we will see how the same set of abstract sounds was used in 2 diﬀerent studies described in sections 4.3 and 4.1. 3.2

Procedure

To control the design of stimuli, it is important to verify in a pre-test that the evaluated sounds are actually “abstract” for most listeners. In a musical context, D. Smalley [35] has introduced the expression “surrogacy” level (or degree) to quantify the ease of source recognition. This level is generally evaluated by using identiﬁcation tasks. In [6], the authors describe three methods: 1) Free identiﬁcation tasks that consists of associating words or any description with sounds [2]. 2) Context-based ratings, which are comparisons between sounds and other stimuli. 3) Attribute rating, which is a generalization of the semantic diﬀerential method. The third method may be the most relevant since it provides graduated ratings on an unlimited number of scales. In particular, we will see in section 4.3 that we evaluated the degree of recognition of abstract sounds (“the sound is easily recognizable or not”) by asking listeners to use a non graduated scale from “not recognizable” to “easily recognizable”. Since abstract sounds are not easily associated with a source (and to the corresponding label), they can also be attributed to several meanings that may depend on the type of experimental procedure and task. In particular, we will see that it is possible to take advantage of this variability of meaning to highlight for example diﬀerences between groups of listeners as described in section 4.1. 3.3

Type of Listening

In general, perception research distinguishes analytic and synthetic listening. Given a listening procedure, subjects may focus on diﬀerent aspects of sounds since diﬀerent concentration and attention levels are involved. From a diﬀerent point of view, [17] introduced the terms “everyday listening” (as opposed to “musical listening”) and argued that even in the case of laboratory experiences, listeners are naturally more interested in sound source properties than in intrinsic properties and therefore use “everyday listening”. [29] also introduced diﬀerent types of listening (“hearing”, “listening”, “comprehending”, “understanding”) and asserted that when listening to a sound we switch from one type of listening to another. Even if diﬀerent points of view are used to deﬁne the diﬀerent types of listening, they share the notions of attentional direction and intention when perceiving sounds. Abstract sounds might help listeners to focus on intrinsic properties of sound and thus to adopt musical listening. Another aspect that could inﬂuence the type of listening and therefore introduce variability in responses is the coexistence of several streams in a sound5 . If a sound is composed of several streams, listeners might alternatively focus on diﬀerent elements which cannot be accurately controlled by the experimenter. 5

Auditory streams have been introduced by Bregman [8], and describe our ability to group/separate diﬀerent elements of a sound.

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Since abstract sounds have no univocal meaning to be preserved, it is possible to proceed to transformations that favour one stream (and alter the original meaning). This is not the case for environmental sound recordings for instance, since transformations can make them unrecognizable. Note that classiﬁcation of sounds with several streams according to Schaeﬀer’s typology might be diﬃcult since they present concomitant proﬁles associated with distinct categories.

4

Potentials of Abstract Sounds

As described in section 2, potentials of abstract sounds was initially revealed in the musical context. In particular, their ability to evoke various emotions was fully investigated by electroacoustic composers. In this section, we describe how abstract sounds can be used in diﬀerent contexts by presenting studies linked to three diﬀerent research domains, i.e. sound synthesis, cognitive neuroscience and clinical diagnosis. Note that we only aim at giving an overview of some experiments that use abstract sounds, in order to discuss the motivations behind the diﬀerent experimental approaches. Details of the material and methods can be found in the referred articles in the following sections. The three experiments partially shared the same stimuli. We collected abstract sounds provided by electroacoustic composers. Composers constitute an original resource of interesting sounds since they have thousands of specially recorded or synthesized sounds, organized and indexed to be included in their compositions. From these databases, we selected a set of 200 sounds6 that best spread out in the typology table proposed by Schaeﬀer (cf. tab 1). A subset of sounds was ﬁnally chosen according to the needs of each study presented in the following paragraphs. 4.1

Bizarre and Familiar Sounds

Abstract sounds are not often heard in our everyday life and could even be completely novel for listeners. Therefore, they might be perceived as “strange” or “bizarre”. As mentioned above, listeners’ judgements of abstract sounds are highly subjective. In some cases, it is possible to use this subjectivity to investigate some speciﬁcities of human perception and in particular, to highlight diﬀerences of sound evaluations between groups of listeners. In particular, the concept of “bizarre” is one important element from standard classiﬁcation of mental disorders (DSM - IV) for schizophrenia [1] pp. 275. An other frequently reported element is the existence of auditory hallucinations7 , i.e. perception without stimulation. From such considerations, we explored the perception of bizarre and familiar sounds in patients with schizophrenia by using both environmental (for their familiar aspect) and abstract sounds (for their bizarre aspect). The procedure consisted in rating sounds on continuous scales according 6 7

Some examples from [23] are available at http://www.sensons.cnrs-mrs.fr/ CMMR07_semiotique/ “[...] auditory hallucinations are by far the most common and characteristic of Schizophrenia.” [1] pp. 275

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to a perceptual dimension labelled by an adjective (by contrast, classical diﬀerential semantic uses an adjective and an antonym to deﬁne the extremes of each scale). Sounds were evaluated on six dimensions along linear scales: “familiar”, “reassuring”, “pleasant”, “bizarre”, “frightening”, “invasive”8. Concerning the abstract sound corpus, we chose 20 sounds from the initial set of 200 sounds by a pre-test on seven subjects and selected sounds that best spread in the space of measured variables (the perceptual dimensions). This preselection was validated by a second pre-test on fourteen subjects that produced similar repartition of the sounds along the perceptual dimensions. Preliminary results showed that the selected sound corpus made it possible to highlight signiﬁcant diﬀerences between patients with schizophrenia and control groups. Further analysis and testing (for instance brain imaging techniques) will be conducted in order to better understand these diﬀerences. 4.2

Reduction of Linguistic Mediation and Access to Diﬀerent Meanings

Within the domain of cognitive neuroscience, a major issue is to determine whether similar neural networks are involved in the allocation of meaning for language and other non-linguistic sounds. A well-known protocol largely used to investigate semantic processing in language, i.e. the semantic priming paradigm [3], has been applied to other stimuli such as pictures, odors and sounds and several studies highlighted the existence of a conceptual priming in a nonlinguistic context (see [32] for a review). One diﬃculty that occurs when considering non-linguistic stimuli, is the potential eﬀect of linguistic mediation. For instance watching a picture of a bird or listening to the song of a bird might automatically activate the verbal label “bird”. In this case, the conceptual priming cannot be considered as purely non-linguistic because of the implicit naming induced by the stimulus processing. Abstract sounds are suitable candidates to weaken this problem, since they are not easily associated with a recognizable source. In [32], the goals were to determine how a sense is attributed to a sound and whether there are similarities between brain processing of sounds and words. For that, a priming protocol was used with word/sound pairs and the degree of congruence between the prime and the target was manipulated. To design stimuli, seventy abstract sounds from the nine ”balanced” (see section 2) categories of Schaeﬀer’s typology table were evaluated in a pre-test to deﬁne the word/sound pairs. The sounds were presented successively to listeners who were asked to write the ﬁrst words that came to their mind after listening. A large variety of words were given by listeners. One of the sounds obtained for instance the following responses: “dry, wildness, peak, winter, icy, polar, cold”. Nevertheless, for most sounds, it was possible to ﬁnd a common word that was accepted as coherent by more than 50% of the listeners. By associating these common words with the abstract sounds, we designed forty-ﬁve related word/sound pairs. The non-related pairs were constructed by recombining words and sounds 8

These are arguable translations from French adjectives: familier, rassurant, plaisant, bizarre, angoissant, envahissant.

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randomly. This step allowed us to validate the abstract sounds since no label referring to the actual source was given. Indeed when listeners are asked to explicitly label abstract sounds, diﬀerent labels that were more related to the sound quality were collected. In a ﬁrst experiment a written word (prime) was visually presented before a sound (target) and subjects had to decide whether or not the sound and the word ﬁt together. In a second experiment, presentation order was reversed (i.e. sound presented before word). Results showed that participants were able to evaluate the semiotic relation between the prime and the target in both sound-word and word-sound presentations with relatively low inter-subject variability and good consistency (see [32] for details on experimental data and related analysis). This result indicated that abstract sounds are suitable for studying conceptual processing. Moreover, their contextualization by the presentation of a word reduced the variability of interpretations and led to a consensus between listeners. The study also revealed similarities in the electrophysiological patterns (Event Related Potentials) between abstract sounds and word targets, supporting the assumption that similar processing is involved for linguistic and non-linguistic sounds. 4.3

Sound Synthesis

Intuitive control of synthesizers through high-level parameters is still an open problem in virtual reality and sound design. Both in industrial and musical contexts, the challenge consists of creating sounds from a semantic description of their perceptual correlates. Indeed, as discussed formerly, abstract sounds can be rich from an acoustic point of view and enable testing of diﬀerent spectrotemporal characteristics at the same time. Thus they might be useful to identify general signal properties characteristic of diﬀerent sound categories. In addition, they are particularly designed for restitution through speakers (as this is the case for synthesizers). For this purpose, we proposed a general methodology based on evaluation and analysis of abstract sounds aiming at identifying perceptually relevant signal characteristics and propose an intuitive synthesis control. Given a set of desired control parameters and a set of sounds, the proposed method consists of asking listeners to evaluate the sounds on scales deﬁned by the control parameters. Sounds with same/diﬀerent values on a scale are then analyzed in order to identify signal correlates. Finally, using feature based synthesis [20], signal transformations are deﬁned to propose an intuitive control strategy. In [23], we addressed the control of perceived movement evoked by monophonic sounds. We ﬁrst conducted a free categorization task asking subjects to group sounds that evoke a similar movement and to label each category. The aim of this method was to identify sound categories to further identify perceptually relevant sound parameters speciﬁc to each category. Sixty-two abstract sounds were considered for this purpose. Based on subjects’ responses, we identiﬁed six main categories of perceived movements: “rotate”, “fall down”, “approach”, “pass by”, “go away”and “go up”and identiﬁed a set of sounds representative of each category. Note that like in the previous studies, the labels given by the subjects did not refer to the sound source but rather to an evocation. Based on this ﬁrst study, we aimed at reﬁning the perceptual characterization of

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movements and identify relevant control parameters. For that, we selected 40 sounds among the initial corpus of 200 sounds. Note that in the case of movement, we are aware that the recognition of the physical sound source can introduce a bias in the evaluation. If the source can be easily identiﬁed, the corresponding movement is more likely to be linked to the source: a car sound only evokes horizontal movement and cannot fall or go up. Thus, we asked 29 listeners to evaluate the 40 sounds through a questionnaire including the two following questions rated on a linear scale: • “Is the sound source recognizable?” (rated on a non graduated scale from “not recognizable” to “easily recognizable”) • “Is the sound natural?” (rated from “natural” to “synthetic”) When the sources were judged “recognizable”, listeners were asked to write a few words to describe the source. We found a correspondence between responses of the two questions: the source is perceived natural as long as it is easily recognized (R=.89). Note that abstract sounds were judged as “synthesized” sounds even if they actually were recordings from vibrating bodies. Finally we asked listeners to characterize the movements evoked by sounds with a drawing interface that allowed representing combination of the elementary movements previously found (sounds can rotate and go-up at the same time) and where drawing parameters correspond to potential control parameters of the synthesizer. Results showed that it was possible to determine the relevant perceptual features and to propose an intuitive control strategy for a synthesizer dedicated to movements evoked by sounds.

5

Conclusion

In this paper, we presented the advantages of using abstract sounds in audio and perception research based on a review of studies in which we exploited their distinctive features. The richness of abstract sounds in terms of their acoustic characteristics and potential evocations open various perspectives. Indeed, they are generally perceived as “unrecognizable”, “synthetic” and “bizarre” depending on context and task and these aspects can be relevant to help listeners to focus on the intrinsic properties of sounds, to orient the type of listening, to evoke speciﬁc emotions or to better investigate individual diﬀerences. Moreover, they constitute a good compromise between elementary and ecological stimuli. We addressed the design of the sound corpus and of speciﬁc procedures for listening tests using abstract sounds. In auditory perception research, sound categories based on well identiﬁed sound sources are most often considered (verbal/non verbal sounds, environmental sounds, music). The use of abstract sounds may allow deﬁning more general sound categories based on other criteria such as listeners’ evocations or intrinsic sound properties. Based on empirical researches from electroacoustic music trends, the sound typology proposed by P. Schaeﬀer should enable the deﬁnition of such new sound categories and may be relevant for future listening tests including any sound. Otherwise, since abstract sounds

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convey multiple information (attribution of several meanings), the procedure is of importance to orient type of listening towards the information that actually is of interest for the experiment. Beyond these considerations, the resulting reﬂections may help us to address more general and fundamental questions related to the determination of invariant signal morphologies responsible for evocations and to which extent “universal” sound morphologies that do not depend on context and type of listening exist.

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19. Hermann, T.: Taxonomy and deﬁnitions for soniﬁcation and auditory display. In: Proceedings of the 14th International Conference on Auditory Display, Paris, France (2008) 20. Hoﬀman, M., Cook, P.R.: Feature-based synthesis: Mapping acoustic and perceptual features onto synthesis parameters. In: Proceedings of the 2006 International Computer Music Conference (ICMC), New Orleans (2006) 21. Jekosch, U.: 8. Assigning Meaning to Sounds - Semiotics in the Context of ProductSound Design. J. Blauert, 193–221 (2005) 22. McKay, C., McEnnis, D., Fujinaga, I.: A large publicly accessible prototype audio database for music research (2006) 23. Merer, A., Ystad, S., Kronland-Martinet, R., Aramaki, M.: Semiotics of sounds evoking motions: Categorization and acoustic features. In: Kronland-Martinet, R., Ystad, S., Jensen, K. (eds.) CMMR 2007. LNCS, vol. 4969, pp. 139–158. Springer, Heidelberg (2008) 24. Micoulaud-Franchi, J.A., Cermolacce, M., Vion-Dury, J.: Bizzare and familiar recognition troubles of auditory perception in patient with schizophrenia (2010) (in preparation) 25. Moore, B.C.J., Tan, C.T.: Perceived naturalness of spectrally distorted speech and music. The Journal of the Acoustical Society of America 114, 408–419 (2003) 26. Peeters, G., Deruty, E.: Automatic morphological description of sounds. In: Acoustics 2008, Paris, France (2008) 27. Ricard, J., Herrera, P.: Morphological sound description computational model and usability evaluation. In: AES 116th Convention (2004) 28. Risset, J.C., Wessel, D.L.: Exploration of timbre by analysis and synthesis. In: Deutsch, D. (ed.) The psychology of music. Series in Cognition and Perception, pp. 113–169. Academic Press, London (1999) 29. Schaeﬀer, P.: Trait´e des objets musicaux. Editions du seuil (1966) 30. Schaeﬀer, P., Reibel, G.: Solf`ege de l’objet sonore. INA-GRM (1967) 31. Schlauch, R.S.: 12 - Loudness. In: Ecological Psychoacoustics, pp. 318–341. Elsevier, Amsterdam (2004) 32. Sch¨ on, D., Ystad, S., Kronland-Martinet, R., Besson, M.: The evocative power of sounds: Conceptual priming between words and nonverbal sounds. Journal of Cognitive Neuroscience 22, 1026–1035 (2010) 33. Shaﬁro, V., Gygi, B.: How to select stimuli for environmental sound research and where to ﬁnd them. Behavior Research Methods, Instruments, & Computers 36, 590–598 (2004) 34. Smalley, D.: Deﬁning timbre — reﬁning timbre. Contemporary Music Review 10, 35–48 (1994) 35. Smalley, D.: Space-form and the acousmatic image. Org. Sound 12, 35–58 (2007) 36. Tanaka, K., Matsubara, K., Sato, T.: Study of onomatopoeia expressing strange sounds: Cases of impulse sounds and beat sounds. Transactions of the Japan Society of Mechanical Engineers C 61, 4730–4735 (1995) 37. Thoresen, L., Hedman, A.: Spectromorphological analysis of sound objects: an adaptation of pierre schaeﬀer’s typomorphology. Organised Sound 12, 129–141 (2007) 38. Zeitler, A., Ellermeier, W., Fastl, H.: Signiﬁcance of meaning in sound quality evaluation. Fortschritte der Akustik, CFA/DAGA 4, 781–782 (2004) 39. Zeitler, A., Hellbrueck, J., Ellermeier, W., Fastl, H., Thoma, G., Zeller, P.: Methodological approaches to investigate the eﬀects of meaning, expectations and context in listening experiments. In: INTER-NOISE 2006, Honolulu, Hawaii (2006)

Pattern Induction and Matching in Music Signals Anssi Klapuri Centre for Digital Music, Queen Mary University of London Mile End Road, E1 4NS London, United Kingdom [email protected] http://www.elec.qmul.ac.uk/people/anssik/

Abstract. This paper discusses techniques for pattern induction and matching in musical audio. At all levels of music - harmony, melody, rhythm, and instrumentation - the temporal sequence of events can be subdivided into shorter patterns that are sometimes repeated and transformed. Methods are described for extracting such patterns from musical audio signals (pattern induction) and computationally feasible methods for retrieving similar patterns from a large database of songs (pattern matching).

1

Introduction

Pattern induction and matching plays an important part in understanding the structure of a given music piece and in detecting similarities between two diﬀerent music pieces. The term pattern is here used to refer to sequential structures that can be characterized by a time series of feature vectors x1 , x2 , . . . , xT . The vectors xt may represent acoustic features calculated at regularly time intervals or discrete symbols with varying durations. Many diﬀerent elements of music can be represented in this form, including melodies, drum patterns, and chord sequences, for example. In order to focus on the desired aspect of music, such as the drums track or the lead vocals, it is often necessary to extract that part from a polyphonic music signal. Section 2 of this paper will discuss methods for separating meaningful musical objects from polyphonic recordings. Contrary to speech, there is no global dictionary of patterns or ”words” that would be common to all music pieces, but in a certain sense, the dictionary of patterns is created anew in each music piece. The term pattern induction here refers to the process of learning to recognize sequential structures from repeated exposure [63]. Repetition plays an important role here: rhythmic patterns are repeated, melodic phrases recur and vary, and even entire sections, such as the chorus in popular music, are repeated. This kind of self-reference is crucial for imposing structure on a music piece and enables the induction of the underlying prototypical patterns. Pattern induction will be discussed in Sec. 3. Pattern matching, in turn, consists of searching a database of music for segments that are similar to a given query pattern. Since the target matches can S. Ystad et al. (Eds.): CMMR 2010, LNCS 6684, pp. 188–204, 2011. c Springer-Verlag Berlin Heidelberg 2011

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in principle be located at any temporal position and are not necessarily scaled to the same length as the query pattern, temporal alignment of the query and target patterns poses a signiﬁcant computational challenge in large databases. Given that the alignment problem can be solved, another pre-requisite for meaningful pattern matching is to deﬁne a distance measure between musical patterns of diﬀerent kinds. These issues will be discussed in Sec. 4. Pattern processing in music has several interesting applications, including music information retrieval, music classiﬁcation, cover song identiﬁcation, and creation of mash-ups by blending matching excerpts from diﬀerent music pieces. Given a large database of music, quite detailed queries can be made, such as searching for a piece that would work as an accompaniment for a user-created melody.

2

Extracting the Object of Interest from Music

There are various levels at which pattern induction and matching can take place in music. At one extreme, a polyphonic music signal is considered as a coherent whole and features describing its harmonic or timbral aspects, for example, are calculated. In a more analytic approach, some part of the signal, such as the melody or the drums, is extracted before the feature calculation. Both of these approaches are valid from the perceptual viewpoint. Human listeners, especially trained musicians, can switch between a ”holistic” listening mode and a more analytic one where they focus on the part played by a particular instrument or decompose music into its constituent elements and their releationships [8,3]. Even when a music signal is treated as a coherent whole, it is necessary to transform the acoustic waveform into a series of feature vectors x1 , x2 , . . . , xT that characterize the desired aspect of the signal. Among the most widely used features are Mel-frequency cepstral coeﬃcients (MFCCs) to represent the timbral content of a signal in terms of its spectral energy distribution [73]. The local harmonic content of a music signal, in turn, is often summarized using a 12dimensional chroma vector that represents the amount of spectral energy falling at each of the 12 tones of an equally-tempered scale [5,50]. Rhythmic aspects are conveniently represented by the modulation spectrum which encodes the pattern of sub-band energy ﬂuctuations within windows of approximately one second in length [15,34]. Besides these, there are a number of other acoustic features, see [60] for an overview. Focusing pattern extraction on a certain instrument or part in polyphonic music requires that the desired part be pulled apart from the rest before the feature extraction. While this is not entirely straightforward in all cases, it enables musically more interesting pattern induction and matching, such as looking at the melodic contour independently of the accompanying instruments. Some strategies towards decomposing a music signal into its constituent parts are discussed in the following.

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Time-Frequency and Spatial Analysis

Musical sounds, like most natural sounds, tend to be sparse in the time-frequency domain, meaning that the sounds can be approximated using a small number of non-zero elements in the time-frequency domain. This facilitates sound source separation and audio content analysis. Usually the short-time Fourier transform (STFT) is used to represent a given signal in the time-frequency domain. A viable alternative for STFT is the constant-Q transform (CQT), where the center frequencies of the frequency bins are geometrically spaced [9,68]. CQT is often ideally suited for the analysis of music signals, since the fundamental frequencies (F0s) of the tones in Western music are geometrically spaced. Spatial information can sometimes be used to organize time-frequency components to their respective sound sources [83]. In the case of stereophonic audio, time-frequency components can be clustered based on the ratio of left-channel amplitude to the right, for example. This simple principle has been demonstrated to be quite eﬀective for some music types, such as jazz [4], despite the fact that overlapping partials partly undermine the idea. Duda et al. [18] used stereo information to extract the lead vocals from complex audio for the purpose of query-by-humming. 2.2

Separating Percussive Sounds from the Harmonic Part

It is often desirable to analyze the drum track of music separately from the harmonic part. The sinusoids+noise model is the most widely-used technique for this purpose [71]. It produces quite robust quality for the noise residual, although the sinusoidal (harmonic) part often suﬀers quality degradation for music with dense sets of sinusoids, such as orchestral music. Ono et al. proposed a method which decomposes the power spectrogram X(F ×T ) of a mixture signal into a harmonic part H and percussive part P so that X = H + P [52]. The decomposition is done by minimizing an objective function that measures variation over time n for the harmonic part and variation over frequency k for the percussive part. The method is straightforward to implement and produces good results. Non-negative matrix factorization (NMF) is a technique that decomposes the spectrogram of a music signal into a linear sum of components that have a ﬁxed spectrum and time-varying gains [41,76]. Helen and Virtanen used the NMF to separate the magnitude spectrogram of a music signal into a couple of dozen components and then used a support vector machine (SVM) to classify each component either to pitched instruments or to drums, based on features extracted from the spectrum and the gain function of each component [31]. 2.3

Extracting Melody and Bass Line

Vocal melody is usually the main focus of attention for an average music listener, especially in popular music. It tends to be the part that makes music memorable and easily reproducible by singing or humming [69].

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Several diﬀerent methods have been proposed for the main melody extraction from polyphonic music. The task was ﬁrst considered by Goto [28] and later various methods for melody tracking have been proposed by Paiva et al. [54], Ellis and Poliner [22], Dressler [17], and Ryyn¨ anen and Klapuri [65]. Typically, the methods are based on framewise pitch estimation followed by tracking or streaming over time. Some methods involve a timbral model [28,46,22] or a musicological model [67]. For comparative evaluations of the diﬀerent methods, see [61] and [www.music-ir.org/mirex/]. Melody extraction is closely related to vocals separation: extracting the melody faciliatates lead vocals separation, and vice versa. Several diﬀerent approaches have been proposed for separating the vocals signal from polyphonic music, some based on tracking the pitch of the main melody [24,45,78], some based on timbre models for the singing voice and for the instrumental background [53,20], and yet others utilizing stereo information [4,18]. Bass line is another essential part in many music types and usually contains a great deal of repetition and note patterns that are rhythmically and tonally interesting. Indeed, high-level features extracted from the bass line and the playing style have been successfully used for music genre classiﬁcation [1]. Methods for extracting the bass line from polyphonic music have been proposed by Goto [28], Hainsworth [30], and Ryyn¨ anen [67]. 2.4

Instrument Separation from Polyphonic Music

For human listeners, it is natural to organize simultaneously occurring sounds to their respective sound sources. When listening to music, people are often able to focus on a given instrument – despite the fact that music intrinsically tries to make co-occurring sounds “blend” as well as possible. Separating the signals of individual instruments from a music recording has been recently studied using various approaches. Some are based on grouping sinusoidal components to sources (see e.g. [10]) whereas some others utilize a structured signal model [19,2]. Some methods are based on supervised learning of instrument-speciﬁc harmonic models [44], whereas recently several methods have been proposed based on unsupervised methods [23,75,77]. Some methods do not aim at separating time-domain signals, but extract the relevant information (such as instrument identities) directly in some other domain [36]. Automatic instrument separation from a monaural or stereophonic recording would enable pattern induction and matching for the individual instruments. However, source separation from polyphonic music is extremely challenging and the existing methods are generally not as reliable as those intended for melody or bass line extraction.

3

Pattern Induction

Pattern induction deals with the problem of detecting repeated sequential structures in music and learning the pattern underlying these repetitions. In the following, we discuss the problem of musical pattern induction from a general

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Fig. 1. A “piano-roll” representation for an excerpt from Mozart’s Turkish March. The vertical lines indicate a possible grouping of the component notes into phrases.

perspective. We assume that a time series of feature vectors x1 , x2 , . . . , xT describing the desired characteristics of the input signal is given. The task of pattern induction, then, is to detect repeated sequences in this data and to learn a prototypical pattern that can be used to represent all its occurrences. What makes this task challenging is that the data is generally multidimensional and real-valued (as opposed to symbolic data), and furthermore, music seldom repeats itself exactly, but variations and transformations are applied on each occurrence of a given pattern. 3.1

Pattern Segmentation and Clustering

The basic idea of this approach is to subdivide the feature sequence x1 , x2 , . . . , xT into shorter segments and then cluster these segments in order to ﬁnd repeated patterns. The clustering part requires that a distance measure between two feature segments is deﬁned – a question that will be discussed separately in Sec. 4 for diﬀerent types of features. For pitch sequences, such as the melody and bass lines, there are well-deﬁned musicological rules how individual sounds are perceptually grouped into melodic phrases and further into larger musical entities in a hierarchical manner [43]. This process is called grouping and is based on relatively simple principles such as preferring a phrase boundary at a point where the time or the pitch interval between two consecutive notes is larger than in the immediate vicinity (see Fig. 1 for an example). Pattern induction, then, proceeds by choosing a certain time scale, performing the phrase segmentation, cropping the pitch sequences according to the shortest phrase, clustering the phrases using for example k-means clustering, and ﬁnally using the pattern nearest to each cluster centroid as the ”prototype” pattern for that cluster. A diﬃculty in implementing the phrase segmentation for audio signals is that contrary to MIDI, note durations and rests are diﬃcult to extract from audio. Nevertheless, some methods produce discrete note sequences from music [67,82], and thus enable segmenting the transcription result into phrases. Musical meter is an alternative criterion for segmenting musical feature sequences into shorter parts for the purpose of clustering. Computational meter analysis usually involves tracking the beat and locating bar lines in music. The

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good news here is that meter analysis is a well-understood and feasible problem for audio signals too (see e.g. [39]). Furthermore, melodic phrase boundaries often co-incide with strong beats, although this is not always the case. For melodic patterns, for example, this segmenting rule eﬀectively requires two patterns to be similarly positioned with respect to the musical measure boundaries in order for them to be similar, which may sometimes be a too strong assumption. However, for drum patterns this requirement is well justiﬁed. Bertin-Mahieux et al. performed harmonic pattern induction for a large database of music in [7]. They calculated a 12-dimensional chroma vector for each musical beat in the target songs. The beat-synchronous chromagram data was then segmented at barline positions and the resulting beat-chroma patches were vector quantized to obtain a couple of hundred prototype patterns. A third strategy is to avoid segmentation altogether by using shift-invariant features. As an example, let us consider a sequence of one-dimensional features x1 , x2 , . . . , xT . The sequence is ﬁrst segmented into partly-overlapping frames that have length approximately the same as the patterns being sought. Then the sequence within each frame is Fourier transformed and the phase information is discarded in order to make the features shift-invariant. The resulting magnitude spectra are then clustered to ﬁnd repeated patterns. The modulation spectrum features (aka ﬂuctuation patterns) mentioned in the beginning of Sec. 2 are an example of such a shift-invariant feature [15,34]. 3.2

Self-distance Matrix

Pattern induction, in the sense deﬁned in the beginning of this section, is possible only if a pattern is repeated in a given feature sequence. The repetitions need not be identical, but bear some similarity with each other. A self-distance matrix (aka self-similarity matrix) oﬀers a direct way of detecting these similarities. Given a feature sequence x1 , x2 , . . . , xT and a distance function d that speciﬁes the distance between two feature vectors xi and xj , the self-distance matrix (SDM) is deﬁned as (1) D(i, j) = d(xi , xj ) for i, j ∈ {1, 2, . . . , T }. Frequently used distance measures include the Euclidean distance xi −xj and the cosine distance 0.5(1−xi , xj /(xi xj )). Repeated sequences appear in the SDM as oﬀ-diagonal stripes. Methods for detecting these will be discussed below. An obvious diﬃculty in calculating the SDM is that when the length T of the feature sequence is large, the number of distance computations T 2 may become computationally prohibitive. A typical solution to overcome this is to use beatsynchronized features: a beat tracking algorithm is applied and the features xt are then calculated within (or averaged over) each inter-beat interval. Since the average inter-beat interval is approximately 0.5 seconds – much larger than a typical analysis frame size – this greatly reduces the number of elements in the time sequence and in the SDM. An added beneﬁt of using beat-synchronous features is that this compensates for tempo ﬂuctuations within the piece under analysis. As a result, repeated sequences appear in the SDM as stripes that run

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Fig. 2. A self-distance matrix for Chopin’s Etude Op 25 No 9, calculated using beatsynchronous chroma features. As the oﬀ-diagonal dark stripes indicate, the note sequence between 1s and 5s starts again at 5s, and later at 28s and 32s in a varied form.

exactly parallel to the main diagonal. Figure 2 shows an example SDM calculated using beat-synchronous chroma features. Self-distance matrices have been widely used for audio-based analysis of the sectional form (structure) of music pieces [12,57]. In that domain, several different methods have been proposed for localizing the oﬀ-diagonal stripes that indicate repeating sequences in the music [59,27,55]. Goto, for example, ﬁrst calculates a marginal histogram which indicates the diagonal bands that contain considerable repetition, and then ﬁnds the beginning and end points of the repeted segments at a second step [27]. Serra has proposed an interesting method for detecting locally similar sections in two feature sequences [70]. 3.3

Lempel-Ziv-Welch Family of Algorithms

Repeated patterns are heavily utilized in universal lossless data compression algorithms. The Lempel-Ziv-Welch (LZW) algorithm, in particular, is based on matching and replacing repeated patterns with code values [80]. Let us denote a sequence of discrete symbols by s1 , s2 , . . . , sT . The algorithm initializes a dictionary which contains codes for individual symbols that are possible at the input. At the compression stage, the input symbols are gathered into a sequence until the next character would make a sequence for which there is no code yet in the dictionary, and a new code for that sequence is then added to the dictionary. The usefulness of the LZW algorithm for musical pattern matching is limited by the fact that it requires a sequence of discrete symbols as input, as opposed to real-valued feature vectors. This means that a given feature vectore sequence has

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to be vector-quantized before processing with the LZW. In practice, also beatsynchronous feature extraction is needed to ensure that the lengths of repeated sequences are not aﬀected by tempo ﬂuctuation. Vector quantization (VQ, [25]) as such is not a problem, but choosing a suitable level of granularity becomes very diﬃcult: if the number of symbols is too large, then two repeats of a certain pattern are quantized dissimilarly, and if the number of symbols is too small, too much information is lost in the quantization and spurious repeats are detected. Another inherent limitation of the LZW family of algorithms is that they require exact repetition. This is usually not appropriate in music, where variation is more a rule than an exception. Moreover, the beginning and end times of the learned patterns are arbitrarily determined by the order in which the input sequence is analyzed. Improvements over the LZW family of algorithms for musical pattern induction have been considered e.g. by Lartillot et al. [40]. 3.4

Markov Models for Sequence Prediction

Pattern induction is often used for the purpose of predicting a data sequence. Ngram models are a popular choice for predicting a sequence of discrete symbols s1 , s2 , . . . , sT [35]. In an N-gram, the preceding N − 1 symbols are used to determine the probabilities for diﬀerent symbols to appear next, P (st |st−1 , . . . , st−N +1 ). Increasing N gives more accurate predictions, but requires a very large amount of training data to estimate the probabilities reliably. A better solution is to use a variable-order Markov model (VMM) for which the context length N varies in response to the available statistics in the training data [6]. This is a very desirable feature, and for note sequences, this means that both short and long note sequences can be modeled within a single model, based on their occurrences in the training data. Probabilistic predictions can be made even when patterns do not repeat exactly. Ryyn¨ anen and Klapuri used VMMs as a predictive model in a method that transcribes bass lines in polyphonic music [66]. They used the VMM toolbox of Begleiter et al. for VMM training and prediction [6]. 3.5

Interaction between Pattern Induction and Source Separation

Music often introduces a certain pattern to the listener in a simpler form before adding further “layers” of instrumentation at subsequent repetitions (and variations) of the pattern. Provided that the repetitions are detected via pattern induction, this information can be fed back in order to improve the separation and analysis of certain instruments or parts in the mixture signal. This idea was used by Mauch et al. who used information about music structure to improve recognition of chords in music [47].

4

Pattern Matching

This section considers the problem of searching a database of music for segments that are similar to a given pattern. The query pattern is denoted by a feature

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Time (target) Fig. 3. A matrix of distances used by DTW to ﬁnd a time-alignment between diﬀerent feature sequences. The vertical axis represents the time in a query excerpt (Queen’s Bohemian Rhapsody). The horizontal axis corresponds to the concatenation of features from three diﬀerent excerps: 1) the query itself, 2) “Target 1” (Bohemian Rhapsody performed by London Symphonium Orchestra) and Target 2 (It’s a Kind of Magic by Queen). Beginnings of the three targets are indicated below the matrix. Darker values indicate smaller distance.

seqence y1 , y2 , . . . , yM , and for convenience, x1 , x2 , . . . , xT is used to denote a concatenation of the feature sequences extracted from all target music pieces. Before discussing the similarity metrics between two music patterns, let us consider the general computational challenges in comparing a query pattern against a large database, an issue that is common to all types of musical patterns. 4.1

Temporal Alignment Problem in Pattern Comparison

Pattern matching in music is computationally demanding, because the query pattern can in principle occur at any position of the target data and because the time-scale of the query pattern may diﬀer from the potential matches in the target data due to tempo diﬀerences. These two issues are here referred to as the time-shift and time-scale problem, respectively. Brute-force matching of the query pattern at all possible locations of the target data and using diﬀerent time-scaled versions of the query pattern would be computationally infeasible for any database of signiﬁcant size. Dynamic time warping (DTW) is a technique that aims at solving both the time-shift and time-scale problem simultaneously. In DTW, a matrix of distances is computed so that element (i, j) of the matrix represents the pair-wise distance between element i of the query pattern and element j in the target data (see Fig. 3 for an example). Dynamic programming is then applied to ﬁnd a path of small distances from the ﬁrst to the last row of the matrix, placing suitable constraints on the geometry of the path. DTW has been used for melodic pattern matching by Dannenberg [13], for structure analysis by Paulus [55], and for cover song detection by Serra [70], to mention a few examples. Beat-synchronous feature extraction is an eﬃcient mechanism for dealing with the time-scale problem, as already discussed in Sec. 3. To allow some further

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ﬂexibility in pattern scaling and to mitigate the eﬀect of tempo estimation errors, it is sometimes useful to further time-scale the beat-synchronized query pattern by factors 12 , 1, and 2, and match each of these separately. A remaining problem to be solved is the temporal shift: if the target database is very large, comparing the query pattern at every possible temporal position in the database can be infeasible. Shift-invariant features are one way of dealing with this problem: they can be used for approximate pattern matching to prune the target data, after which the temporal alignment is computed for the bestmatching candidates. This allows the ﬁrst stage of matching to be performed an order of magnitude faster. Another potential solution for the time-shift problem is to segment the target database by meter analysis or grouping analysis, and then match the query pattern only at temporal positions determined by estimated bar lines or group boundaries. This approach was already discussed in Sec. 3. Finally, eﬃcient indexing techniques exist for dealing with extremely large databases. In practice, these require that the time-scale problem is eliminated (e.g. using beat-synchronous features) and the number of time-shifts is greatly reduced (e.g. using shift-invariant features or pre-segmentation). If these conditions are satisﬁed, the locality sensitive hashing (LSH) for example, enables sublinear search complexity for retrieving the approximate nearest neighbours of the query pattern from a large database [14]. Ryynanen et al. used LSH for melodic pattern matching in [64]. 4.2

Melodic Pattern Matching

Melodic pattern matching is usually considered in the context of query-byhumming (QBH), where a user’s singing or humming is used as a query to retrieve music with a matching melodic fragment. Typically, the user’s singing is ﬁrst transcribed into a pitch trajectory or a note sequence before the matching takes place. QBH has been studied for more than 15 years and remains an active research topic [26,48]. Research on QBH originated in the context of the retrieval from MIDI or score databases. Matching approaches include string matching techniques [42], hidden Markov models [49,33], dynamic programming [32,79], and eﬃcient recursive alignment [81]. A number of QBH systems have been evaluated in Music Information Retrieval Evaluation eXchange (MIREX) [16]. Methods for the QBH of audio data have been proposed only quite recently [51,72,18,29,64]. Typically, the methods extract the main melodies from the target musical audio (see Sec. 2.3) before the matching takes place. However, it should be noted that a given query melody can in principle be matched directly against polyphonic audio data in the time-frequency or time-pitch domain. Some on-line services incorporating QBH are already available, see e.g. [www.midomi.com], [www.musicline.de], [www.musipedia.org]. Matching two melodic patterns requires a proper deﬁnition of similarity. The trivial assumption that two patterns are similar if they have identical pitches is usually not appropriate. There are three main reasons that cause the query

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pattern and the target matches to diﬀer: 1) low quality of the sung queries (especially in the case of musically untrained users), 2) errors in extracting the main melodies automatically from music recordings, and 3) musical variation, such as fragmentation (elaboration) or consolidation (reduction) of a given melody [43]. One approach that works quite robustly in the presence of all these factors is to calculate Euclidean distance between temporally aligned log-pitch trajectories. Musical key normalization can be implemented simply by normalizing the two pitch contours to zero mean. More extensive review of research on melodic similarity can be found in [74]. 4.3

Patterns in Polyphonic Pitch Data

Instead of using only the main melody for music retrieval, polyphonic pitch data can be processed directly. Multipitch estimation algorithms (see [11,38] for review) can be used to extract multiple pitch values in successive time frames, or alternatively, a mapping from time-frequency to a time-pitch representation can be employed [37]. Both of these approaches yield a representation in the timepitch plane, the diﬀerence being that multipitch estimation algorithms yield a discrete set of pitch values, whereas mapping to a time-pitch plane yields a more continuous representation. Matching a query pattern against a database of music signals can be carried out by a two-dimensional correlation analysis in the time-pitch plane. 4.4

Chord Sequences

Here we assume that chord information is represented as a discrete symbol sequence s1 , s2 , . . . , sT , where st indicates the chord identity at time frame t.

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Measuring the distance between two chord sequences requires that the distance between each pair of diﬀerent chords is deﬁned. Often this distance is approximated by arranging chords in a one- or two-dimensional space, and then using the geometric distance between chords in this space as the distance measure [62], see Fig. 4 for an example. In the one-dimensional case, the circle of ﬁfths is often used. It is often useful to compare two chord sequences in a key-invariant manner. This can be done by expressing chords in relation to tonic (that is, using chord degrees instead of the “absolute” chords), or by comparing all the 12 possible transformations and choosing the minimum distance. 4.5

Drum Patterns and Rhythms

Here we discuss pattern matching in drum tracks that are presented as acoustic signals and are possibly extracted from polyphonic music using the methods described in Sec. 2.2. Applications of this include for example query-by-tapping [www.music-ir.org/mirex/] and music retrieval based on drum track similarity. Percussive music devoid of both harmony and melody can contain considerable amount of musical form and structure, encoded into the timbre, loudness, and timing relationships between the component sounds. Timbre and loudness characteristics can be conveniently represented by MFCCs extracted in successive time frames. Often, however, the absolute spectral shape and loudness of the components sounds is not of interest, but instead, the timbre and loudness of sounds relative to each other deﬁnes the perceived rhythm. Paulus and Klapuri reduced the rhythmic information into a two-dimensional signal describing the evolution of loudness and spectral centroid over time, in order to compare rhythmic patterns performed using an arbitrary set of sounds [56]. The features were mean- and variance-normalized to allow comparison across diﬀerent sound sets, and DTW was used to align the two patterns under comparison. Ellis and Arroyo projected drum patterns into a low-dimensional representation, where diﬀerent rhythms could be represented as a linear sum of so-called eigenrhythms [21]. They collected 100 drum patterns from popular music tracks and estimated the bar line positions in these. Each pattern was normalized and the resulting set of patterns was subjected to principal component analysis in order to obtain a set of basis patterns (”eigenrhythms”) that were then combined to approximate the original data. The low-dimensional representation of the drum patterns was used as a space for classiﬁcation and for measuring similarity between rhythms. Non-negative matrix factorization (NMF, see Sec. 2.2) is another technique for obtaining a mid-level representation for drum patterns [58]. The resulting component gain functions can be subjected to the eigenrhythm analysis described above, or statistical measures can be calculated to characterize the spectra and gain functions for rhythm comparison.

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Conclusions

This paper has discussed the induction and matching of sequential patterns in musical audio. Such patterns are neglected by the commonly used ”bag-offeatures” approach to music retrieval, where statistics over feature vectors are calculated to collapse the time structure altogether. Processing sequentical structures poses computational challenges, but also enables musically interesting retrieval tasks beyond those possible with the bag-of-features approach. Some of these applications, such as query-by-humming services, are already available for consumers. Acknowledgments. Thanks to Jouni Paulus for the Matlab code for computing self-distance matrices. Thanks to Christian Dittmar for the idea of using repeated patterns to improve the accuracy of source separation and analysis.

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Unsupervised Analysis and Generation of Audio Percussion Sequences Marco Marchini and Hendrik Purwins Music Technology Group, Department of Information and Communications Technologies, Universitat Pompeu Fabra Roc Boronat, 138, 08018 Barcelona, Spain Tel.: +34-93 542-1365; Fax: +34-93 542-2455 {marco.marchini,hendrik.purwins}@upf.edu

Abstract. A system is presented that learns the structure of an audio recording of a rhythmical percussion fragment in an unsupervised manner and that synthesizes musical variations from it. The procedure consists of 1) segmentation, 2) symbolization (feature extraction, clustering, sequence structure analysis, temporal alignment), and 3) synthesis. The symbolization step yields a sequence of event classes. Simultaneously, representations are maintained that cluster the events into few or many classes. Based on the most regular clustering level, a tempo estimation procedure is used to preserve the metrical structure in the generated sequence. Employing variable length Markov chains, the ﬁnal synthesis is performed, recombining the audio material derived from the sample itself. Representations with diﬀerent numbers of classes are used to trade oﬀ statistical signiﬁcance (short context sequence, low clustering reﬁnement) versus speciﬁcity (long context, high clustering reﬁnement) of the generated sequence. For a broad variety of musical styles the musical characteristics of the original are preserved. At the same time, considerable variability is introduced in the generated sequence. Keywords: music analysis, music generation, unsupervised clustering, Markov chains, machine listening.

1

Introduction

In the eighteenth century, composers such as C. P. E. Bach and W. A. Mozart used the Musikalisches W¨ urfelspiel as a game to create music. They composed several bars of music that could be randomly recombined in various ways, creating a new “composition” [3]. In the 1950s, Hiller and Isaacson’s automatically composed the Illiac Suite and Xenakis’ used Markov chains and stochastic processes in his compositions. Probably one of the most extensive work in style imitation is the one by David Cope [3]. He let the computer compose compositions in the style of Beethoven, Prokoﬁev, Chopin, and Rachmaninoﬀ. Pachet [13] developed the Continuator, a MIDI-based system for real-time interaction S. Ystad et al. (Eds.): CMMR 2010, LNCS 6684, pp. 205–218, 2011. c Springer-Verlag Berlin Heidelberg 2011

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with musicians, producing jazz-style music. Another system with the same characteristics as the Continuator, called OMax, was able to learn an audio stream employing an indexing procedure explained in [5]. Hazan et al. [8] built a system which ﬁrst segments the musical stream and extracts timbre and onsets. An unsupervised clustering process yields a sequence of symbols that is then processed by n-grams. The method by Marxer and Purwins [12] consists of a conceptual clustering algorithm coupled with a hierarchical N-gram. Our method presented in this article was ﬁrst described in detail in [11]. First, we deﬁne the system design and the interaction of its parts. Starting from low-level descriptors, we translate them into a “fuzzy score representation”, where two sounds can either be discretized yielding the same symbol or yielding diﬀerent symbols according to which level of interpretation is chosen (Section 2). Then we perform skeleton subsequence extraction and tempo detection to align the score to a grid. At the end, we get a homogeneous sequence in time, on which we perform the prediction. For the generation of new sequences, we reorder the parts of the score, respecting the statistical properties of the sequence while at the same time maintaining the metrical structure (Section 3). In Section 4, we discuss an example.

2

Unsupervised Sound Analysis

As represented in Figure 1, the system basically detects musical blocks in the audio and re-shuﬄes them according to meter and statistical properties of the sequence. We will now describe each step of the process in detail.

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Fig. 1. General architecture of the system

2.1

Segmentation

First, the audio input signal is analyzed by an onset detector that segments the audio ﬁle into a sequence of musical events. Each event is characterized by its position in time (onset) and an audio segment, the audio signal starting at the onset position and ending at the following contiguous onset. In the further processing, these events will serve two purposes. On one side, the events are stored as an indexed sequence of audio fragments which will be used for the resynthesis in the end. On the other side, these events will be compared with each other to generate a reduced score-like representation of the percussion patterns to base a tempo analysis on (cf. Fig. 1 and Sec. 2.2).

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We used the onset detector implemented in the MIR toolbox [9] that is based only on the energy envelope, which proves to be suﬃcient for our purpose of analyzing percussion sounds. 2.2

Symbolization

We will employ segmentation and clustering in order to transform the audio signal into a discrete sequence of symbols (as shown in Fig. 3), thereby facilitating statistical analysis. However, some considerations should be made. As we are not restricting the problem to a monophonic percussion sequence, non-trivial problems arise when one wants to translate a sequence of events into a meaningful symbolic sequence. One would like to decide whether or not two sounds have been played by the same percussion instrument (e.g. snare, bass drum, open hi hat. . . ) and, more speciﬁcally, if two segments contain the same sound in case of polyphony. With a similarity distance we can derive a value representing the similarity between two sounds but when two sounds are played simultaneously a diﬀerent sound may be created. Thus, a sequence could exist that allows for multiple interpretations since the system is not able to determine whether a segment contains one or more sounds played synchronously. A way to avoid this problem directly and to still get a useful representation is to use a fuzzy representation of the sequence. If we listen to each segment very detailedly, every segment may sound diﬀerent. If we listen very coarsely, they may all sound the same. Only listening with an intermediate level of reﬁnement yields a reasonable diﬀerentiation in which we recognize the reoccurrence of particular percussive instruments and on which we can perceive meaningful musical structure. Therefore, we propose to maintain diﬀerent levels of clustering reﬁnement simultaneously and then select the level on which we encounter the most regular non-trivial patterns. In the sequel, we will pursue an implementation of this idea and describe the process in more detail. Feature Extraction. We have chosen to deﬁne the salient part of the event as the ﬁrst 200 ms after the onset position. This duration value is a compromise between capturing enough information about the attack for representing the sound reliably and still avoiding irrelevant parts at the end of the segment which may be due to pauses or interfering other instruments. In the case that the segment is shorter than 200 ms, we use the entire segment for the extraction of the feature vector. Across the salient part of the event we calculate the Mel Frequency Cepstral Coeﬃcient (MFCC) vector frame-by-frame. Over all MFCCs of the salient event part, we take the weighted mean, weighted by the RMS energy of each frame. The frame rate is 100 frame for second, the FFT size is 512 samples and the window size 256. Sound Clustering. At this processing stage, each event is characterized by a 13-dimensional vector (and the onset time). Events can thus be seen as points in a 13-dimensional space in which a topology is induced by the Euclidean distance.

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We used the single linkage algorithm to discover event clusters in this space (cf. [6] for details). This algorithm recursively performs clustering in a bottomup manner. Points are grouped into clusters. Then clusters are merged with additional points and clusters are merged with clusters into super clusters. The distance between two clusters is deﬁned as the shortest distance between two points, each being in a diﬀerent cluster, yielding a binary tree representation of the point similarities (cf. Fig. 2). The leaf nodes correspond to single events. Each node of the tree occurs at a certain height, representing the distance between the two child nodes. Figure 2 (top) shows an example of a clustering tree of the onset events of a sound sequence.

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Fig. 2. A tree representation of the similarity relationship between events (top) of an audio percussion sequence (bottom). The threshold value chosen here leads to a particular cluster conﬁguration. Each cluster with more than one instance is indicated by a colored subtree. The events in the audio sequence are marked in the colors of the clusters they belong to. The height of each node is the distance (according to the single linkage criterion) between its two child nodes. Each of the leaf nodes on the bottom of the graph corresponds to an event.

The height threshold controls the (number of) clusters. Clusters are generated with inter-cluster distances higher than the height threshold. Two thresholds lead to the same cluster conﬁguration if and only if their values are both within the range delimited by the previous lower node and the next upper node in the tree. It is therefore evident that by changing the height threshold, we can get as many diﬀerent cluster conﬁgurations as the number of events we have in the sequence. Each cluster conﬁguration leads to a diﬀerent symbol alphabet

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size and therefore to a diﬀerent symbol sequence representing the original audio ﬁle. We will refer to those sequences as representation levels or simply levels. These levels are implicitly ordered. On the leaf level at the bottom of the tree we ﬁnd the lowest inter-cluster distances, corresponding to a sequence with each event being encoded by a unique symbol due to weak quantization. On the root level on top of the tree we ﬁnd the cluster conﬁguration with the highest intercluster distances, corresponding to a sequence with all events denoted by the same symbol due to strong quantization. Given a particular level, we will refer to the events denoted by the same symbol as the instances of that symbol. We do not consider the implicit inheritance relationships between symbols of diﬀerent levels.

Fig. 3. A continuous audio signal (top) is discretized via clustering yielding a sequence of symbols (bottom). The colors inside the colored triangles denote the cluster of the event, related to the type of sound, i.e. bass drum, hi-hat, or snare.

2.3

Level Selection

Handling diﬀerent representations of the same audio ﬁle in parallel enables the system to make predictions based on ﬁne or coarse context structure, depending on the situation. As explained in the previous section, if the sequence contains n events the number of total possible distinct levels is n. As the number of events increases, it is particularly costly to use all this levels together because the number of levels also increases linearly with the number of onsets. Moreover, as it will be clearer later, this representation will lead to over-ﬁtted predictions of new events. This observation leads to the necessity to only select a few levels that can be considered representative of the sequence in terms of structural regularity. Given a particular level, let us consider a symbol σ having at least four instances but not more than 60% of the total number of events and let us call such a symbol an appropriate symbol. The instances of σ deﬁne a subsequence of all the events that is supposedly made of more or less similar sounds according to the degree of reﬁnement of the level. Let us just consider the sequence of

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onsets given by this subsequence. This sequence can be seen as a set of points on a time line. We are interested to quantify the degree of temporal regularity of those onsets. Firstly, we compute the histogram1 of the time diﬀerences (CIOIH) between all possible combinations of two onsets (middle Fig. 4). What we obtain is a sort of harmonic series of peaks that are more or less prominent according to the self-similarity of the sequence on diﬀerent scales. Secondly, we compute the autocorrelation ac(t) (where t is the time in seconds) of the CIOIH which, in case of a regular sequence, has peaks at multiples of its tempo. Let tusp be the positive time value corresponding to its upper side peak. Given the sequence of m onsets x = (x1 , . . . , xm ) we deﬁne the regularity of the sequence of onsets x to be: ac(tusp ) Regularity(x) = 1 tusp log(m) ac(t)dt tusp 0 This deﬁnition was motivated by the observation that the higher this value the more equally the onsets are spaced in time. The logarithm of the number of onsets was multiplied by the ratio to give more importance to symbols with more instances.

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Fig. 4. The procedure applied for computing the regularity value of an onset sequence (top) is outlined. Middle: the histogram of the complete IOI between onsets. Bottom: the autocorrelation of the histogram is shown for a subrange of IOI with relevant peaks marked.

Then we extended, for each level, the regularity concept to an overall regularity of the level. This simply corresponds to the mean of the regularities for all the appropriate symbols of the level. The regularity of the level is deﬁned to be zero in case there is no appropriate symbol. 1

We used a discretization of 100 ms for the onset bars.

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After the regularity value has been computed for each level, we yield the level where the maximum regularity is reached. The resulting level will be referred so as the regular level. We also decided to keep the levels where we have a local maximum because they generally refer to the levels where a partially regular interpretation of the sequence is achieved. In the case where consecutive levels of a sequence share the same regularity only the one is kept that is derived from a higher cluster distance threshold. Figure 5 shows the regularity of the sequence for diﬀerent levels. 4

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Regularity of the Sequence

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Fig. 5. Sequence regularity for a range of cluster distance thresholds (x-axis). An ENST audio excerpt was used for the analysis. The regularity reaches its global maximum value in a central position. Towards the right, regularity increases and then remains constant. The selected peaks are marked with red crosses implying a list of cluster distance threshold values.

2.4

Beat Alignment

In order to predict future events without breaking the metrical structure we use a tempo detection method and introduce a way to align onsets to a metrical grid, accounting for the position of the sequence in the metrical context. For our purpose of learning and regenerating the structure statistically, we do not require a perfect beat detection. Even if we detect a beat that is twice, or half as fast as the perceived beat, or that mistakes an on-beat for an oﬀ-beat, our system could still tolerate this for the analysis of a music fragment, as long as the inter beat interval and the beat phase is always misestimated in the same way. Our starting point is the regular level that has been found with the procedure explained in the previous subsection. On this level we select the appropriate symbol with the highest regularity value. The subsequence that carries this symbol

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will be referred to as the skeleton subsequence since it is like an anchor structure to which we relate our metrical interpretation of the sequence. Tempo Alignment (Inter Beat Interval and Beat Phase). Once the skeleton subsequence is found, the inter beat interval is estimated with the procedure explained in [4]. The tempo is detected considering the intervals between all possible onset pairs of the sequence using a score voting criterion. This method gives higher scores to the intervals that occur more often and that are related by integer ratios to other occurring inter onset intervals. Then the onsets of the skeleton subsequence are parsed in order to detect a possible alignment of the grid to the sequence. We allow a tolerance of 6% the duration of the inter beat interval for the alignment of an onset to the grid position. We chose the interpretation that aligns the highest number of instances to the grid. After discarding the onsets that are not aligned we obtain the preliminary skeleton grid. In Fig. 6 the procedure is visually explicated.

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Fig. 6. Above, a skeleton sequence is represented on a timeline. Below, three possible alignments of the sequences are given by Dixon’s method [4]. All these alignments are based on the same inter beat interval but on diﬀerent beat phases. Each alignment captures a particular onset subset (represented by a particular graphic marker) of the skeleton sequence and discards the remaining onsets of the skeleton sequence. The beat phase that allows to catch the highest number of onsets (the ﬁlled red crosses) is selected and the remaining onsets are removed, thereby yielding the preliminary skeleton grid.

Creation of Skeleton Grid. The preliminary skeleton grid is a sequence of onsets spaced at multiples of a constant time interval, the inter beat interval. But, as shown in Fig. 6, it can still have some gaps (due to missing onsets). The missing onsets are, thus, detected and, in a ﬁrst attempt, the system tries to align the missing onsets with any onset of the entire event sequence (not just the one symbol forming the preliminary skeleton grid). If there is any onset within a tolerance range of ±6% of the inter beat interval of the expected beat position, the expected onset will be aligned to this onset. If no onset within this tolerance range is encountered, the system creats a (virtual) grid bar in the expected beat position. At the end of this completion procedure, we obtain a quasi-periodic skeleton grid, a sequence of beats (events) sharing the same metrical position (the same metrical phase).

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Fig. 7. The event sequence derived from a segmentation by onset detection is indicated by triangles. The vertical lines show the division of the sequence into blocks of homogeneous tempo. The red solid lines represent the beat position (as obtained by the skeleton subsequence). The other black lines (either dashed if aligned to a detected onset or dotted if no close onset is found) represent the subdivisions of the measure into four blocks.

Because of the tolerance used for building such a grid it could be noticed that sometimes the eﬀective measure duration could be slightly longer or slightly shorter. This implements the idea that the grid should be elastic in the sense that, up to a certain degree, it adapts to the (expressive) timing variations of the actual sequence. The skeleton grid catches a part of the complete list of onsets, but we would like to built a grid where most of the onsets are aligned. Thereafter, starting from the skeleton grid, the intermediate point between every two subsequent beats is found and aligned with an onset (if it exists in a tolerance region otherwise a place-holding onset is added). The procedure is recursively repeated until at least 80% of the onsets are aligned to a grid position or the number of created onsets exceeds the number of total onsets. In Fig. 7, an example is presented along with the resulting grid where the skeleton grid, its aligned, and the non-aligned subdivisions are indicated by diﬀerent line markers. Note that, for the sake of simplicity, our approach assumes that the metrical structure is binary. This causes the sequence to be eventually split erroneously. However, we will see in a ternary tempo example that this is not a limiting factor for the generation because the statistical representation somehow compensates for it even if less variable generations are achieved. A more general approach could be implemented with little modiﬁcations. The ﬁnal grid is made of blocks of time of almost equal duration that can contain none, one, or more onset events. It is important that the sequence given to the statistical model is almost homogeneous in time so that a certain number of blocks corresponds to a deﬁned time duration. We used the following rules to assign a symbol to a block (cf. Fig 7): – blocks starting on an aligned onset are denoted by the symbol of the aligned onset, – blocks starting on a non-aligned grid position are denoted by the symbol of the previous block. Finally, a metrical phase value is assigned to each block describing the number of grid positions passed after the last beat position (corresponding to the metrical

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position of the block). For each representation level the new representation of the sequence will be the Cartesian product of the instrument symbol and the phase.

3

Statistical Model Learning

Now we statistically analyze the structure of the symbol sequence obtained in the last section. We employ variable length Markov chains (VLMC) for the statistical analysis of the sequences. In [2,15], a general method for inferencing long sequences is described. For faster computation, we use a simpliﬁed implementation as described in [13]. We construct a suﬃx tree for each level based on the sequence of that level. Each node of the tree represents a speciﬁc context that had occurred in the past. In addition, each node carries a list of continuation indices corresponding to block indices matching the context. For audio, a diﬀerent approach has been applied in [5]. This method does not require an event-wise symbolic representation as it employs the factor oracle algorithm. VLMC has not been applied to audio before, because of the absence of an event-wise symbolic representation we presented above. 3.1

Generation Strategies

If we ﬁx a particular level the continuation indices are drawn according to a posterior probability distribution determined by the longest context found. But which level should be chosen? Depending on the sequence, it could be better to do predictions based either on a coarse or a ﬁne level but it is not clear which one should be preferred. First, we selected the lower level at which a context of at least ˆl existed (for a predetermined ﬁxed ˆl, usually ˆl equal 3 or 4). This works quite good for many examples. But in some cases a context of that length does not exist and the system often reaches the higher level where too many symbols are provided inducing too random generations. On the other side, it occurs very often that the lower level is made of singleton clusters that have only one instance. In this case, a long context is found in the lower level but since a particular symbol sequence only occurs once in the whole original segment the system replicates the audio in the same order as the original. This behavior often leads to the exact reproduction of the original until reaching its end and then a jump at random to another block in the original sequence. In order to increase recombination of blocks and still provide good continuation we employ some heuristics taking into account multiple levels for the prediction. We set p to be a recombination value between 0 and 1. We also need to preprocess the block sequence to prevent arriving at the end of the sequence without any musically meaningful continuation. For this purpose, before learning the sequence, we remove the last blocks until the remaining sequence ends with a context of at least length two. We make use of the following heuristics to generate the continuation in each step:

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– Set a maximal context length ˆ l and compute the list of indices for each level using the appropriate suﬃx tree. Store the achieved length of the context for each level. – Count the number of indices provided by each level. Select only the levels that provide less than 75% the total number of blocks. – Among these level candidates, select only the ones that have the longest context. – Merge all the continuation indices across the selected levels and remove the trivial continuation (the next onset). – In case there is no level providing such a context and the current block is not the last, use the next block as a continuation. – Otherwise, decide randomly with probability p whether to select the next block or rather to generate the actual continuation by selecting randomly between the merged indices.

4

Evaluation and Examples

We tested the system on two audio data bases. The ﬁrst one is the ENST database (see [7]) that provided a collection of around forty drum recording examples. For a descriptive evaluation, we asked two professional percussionists to judge several examples of generations as if they were performances of a student. Moreover, we asked one of them to record beat boxing excerpts trying to push the system to the limits of complexity and to critically assess the sequences that the system had generated from these recordings. The evaluations of the generations created from the ENST examples revealed that the style of the original had been maintained and that the generations had a high degree of interestingness [10]. Some examples are available on the website [1] along with graphical animations visualizing the analysis process. In each video, we see the original sound fragment and the generation derived from it. The horizontal axis corresponds to the time in seconds and the vertical axis to the clustering quantization resolution. Each video shows an animated graphical representation in which each block is represented by a triangle. At each moment, the context and the currently played block is represented by enlarged and colored triangles. In the ﬁrst part of the video, the original sound is played and the animation shows the extracted block representation. The currently played block is represented by an enlarged colored triangle and highlighted by a vertical dashed red line. The other colored triangles highlight all blocks from the starting point of the bar up to the current block. In the second part of the video, only the skeleton subsequence is played. The sequence on top is derived from applying the largest clustering threshold (smallest number of clusters) and the one on the bottom corresponds to the lowest clustering threshold (highest number of clusters). In the ﬁnal part of the video, the generation is shown. The colored triangles

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represent the current block and the current context. The size of the colored triangles decreases monotonically from the current block backwards displaying the past time context window considered by the system. The colored triangles appear only on the levels selected by the generation strategy. In Figure 8, we see an example of successive states of the generation. The levels used by the generator to compute the continuation and the context are highlighted showing colored triangles that decrease in size from the largest, corresponding to the current block, to the smallest that is the furthest past context block considered by the system. In Frame I, the generation starts with block no 4, belonging to the event class indicated by light blue. In the beginning, no previous context is considered for the generation. In Frame II, a successive block no 11 of the green event class has been selected using all ﬁve levels α - and a context history of length 1 just consisting of block no 4 of the light blue event class. Note that the context given by only one light blue block matches the continuation no 11, since the previous block (no 10) is also denoted by light blue at all the ﬁve levels. In Frame III, the context is the bi-gram of the event classes light blue (no 4) and green (no 11). Only level α is selected since at all other levels the bi-gram that corresponds to the colors light blue and green appears only once. However, at level α the system ﬁnds three matches (blocks no 6, 10 and 12) and randomly selects no 10. In Frame IV, the levels diﬀer in the length of the maximal past context. At level α one but only one match (no 11) is found for the 3-gram light blue - green - light blue, and thus this level is discarded. At levels β, γ and δ, no matches for 3-grams are found, but all these levels include 2 matches (block no 5 and 9) for the bi-gram (green - light blue). At level , no match is found for a bi-gram either, but 3 occurrences of the light blue triangle are found.

5

Discussion

Our system eﬀectively generates sequences respecting the structure and the tempo of the original sound fragment for medium to high complexity rhythmic patterns. A descriptive evaluation of a professional percussionist conﬁrmed that the metrical structure is correctly managed and that the statistical representation generates musically meaningful sequences. He noticed explicitly that the drum fills (short musical passages which help to sustain the listener’s attention during a break between the phrases) were handled adequately by the system. The critics by the percussionist were directed to the lack of dynamics, agogics and musically meaningful long term phrasing which we did not address in our approach. Part of those feature could be achieved in the future by extending the system to the analysis of non-binary meter. To achieve musically sensible dynamics and agogics (rallentando, accelerando, rubato. . . ) of the generated musical continuation for example by extrapolation [14] remains a challenge for future work.

Fig. 8. Nine successive frames of the generation. The red vertical dashed line marks the currently played event. In each frame, the largest colored triangle denotes the last played event that inﬂuences the generation of the next event. The size of the triangles decreases going back in time. Only for the selected levels the triangles are enlarged. We can see how the length of the context as well as the number of selected levels dynamically change during the generation. Cf. Section 4 for a detailed discussion of this ﬁgure.

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Acknowledgments Many thanks to Panos Papiotis for his patience during lengthy recording sessions and for providing us with beat boxing examples, the evaluation feedback, and inspiring comments. Thanks a lot to Ricard Marxer for his helpful support. The ﬁrst author (MM) expresses his gratitude to Mirko Degli Esposti and Anna Rita Addessi for their support and for motivating this work. The second author (HP) was supported by a Juan de la Cierva scholarship of the Spanish Ministry of Science and Innovation.

References 1. (December 2010), www.youtube.com/user/audiocontinuation 2. Buhlmann, P., Wyner, A.J.: Variable length markov chains. Annals of Statistics 27, 480–513 (1999) 3. Cope, D.: Virtual Music: Computer Synthesis of Musical Style. MIT Press, Cambridge (2004) 4. Dixon, S.: Automatic extraction of tempo and beat from expressive performances. Journal of New Music Research 30(1), 39–58 (2001) 5. Dubnov, S., Assayag, G., Cont, A.: Audio oracle: A new algorithm for fast learning of audio structures. In: Proceedings of International Computer Music Conference (ICMC), pp. 224–228 (2007) 6. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern classiﬁcation. Wiley, Chichester (2001) 7. Gillet, O., Richard, G.: Enst-drums: an extensive audio-visual database for drum signals processing. In: ISMIR, pp. 156–159 (2006) 8. Hazan, A., Marxer, R., Brossier, P., Purwins, H., Herrera, P., Serra, X.: What/when causal expectation modelling applied to audio signals. Connection Science 21, 119– 143 (2009) 9. Lartillot, O., Toiviainen, P., Eerola, T.: A matlab toolbox for music information retrieval. In: Annual Conference of the German Classiﬁcation Society (2007) 10. Marchini, M.: Unsupervised Generation of Percussion Sequences from a Sound Example. Master’s thesis (2010) 11. Marchini, M., Purwins, H.: Unsupervised generation of percussion sound sequences from a sound example. In: Sound and Music Computing Conference (2010) 12. Marxer, R., Purwins, H.: Unsupervised incremental learning and prediction of audio signals. In: Proceedings of 20th International Symposium on Music Acoustics (2010) 13. Pachet, F.: The continuator: Musical interaction with style. In: Proceedings of ICMC, pp. 211–218. ICMA (2002) 14. Purwins, H., Holonowicz, P., Herrera, P.: Polynomial extrapolation for prediction of surprise based on loudness - a preliminary study. In: Sound and Music Computing Conference, Porto (2009) 15. Ron, D., Singer, Y., Tishby, N.: The power of amnesia: learning probabilistic automata with variable memory length. Mach. Learn. 25(2-3), 117–149 (1996)

Identifying Attack Articulations in Classical Guitar ¨ Tan Hakan Ozaslan, Enric Guaus, Eric Palacios, and Josep Lluis Arcos IIIA, Artiﬁcial Intelligence Research Institute CSIC, Spanish National Research Council Campus UAB, 08193 Bellaterra, Spain {tan,eguaus,epalacios,arcos}@iiia.csic.es

Abstract. The study of musical expressivity is an active ﬁeld in sound and music computing. The research interest comes from diﬀerent motivations: to understand or model music expressivity; to identify the expressive resources that characterize an instrument, musical genre, or performer; or to build synthesis systems able to play expressively. Our research is focused on the study of classical guitar and deals with modeling the use of the expressive resources in the guitar. In this paper, we present a system that combines several state of the art analysis algorithms to identify guitar left hand articulations such as legatos and glissandos. After describing the components of our system, we report some experiments with recordings containing single articulations and short melodies performed by a professional guitarist.

1

Introduction

Musical expressivity can be studied by analyzing the diﬀerences (deviations) between a musical score and its execution. These deviations are mainly motivated by two purposes: to clarify the musical structure [26,10,23] and as a way to communicate aﬀective content [16,19,11]. Moreover, these expressive deviations vary depending on the musical genre, the instrument, and the performer. Speciﬁcally, each performer has his/her own unique way to add expressivity by using the instrument. Our research on musical expressivity aims at developing a system able to model the use of the expressive resources of a classical guitar. In guitar playing, both hands are used: one hand is used to press the strings in the fretboard and the other to pluck the strings. Strings can be plucked using a single plectrum called a ﬂatpick or by directly using the tips of the ﬁngers. The hand that presses the frets is mainly determining the notes while the hand that plucks the strings is mainly determining the note onsets and timbral properties. However, left hand is also involved in the creation of a note onset or diﬀerent expressive articulations like legatos, glissandos, and vibratos. Some guitarists use the right hand to pluck the strings whereas others use the left hand. For the sake of simplicity, in the rest of the document we consider the S. Ystad et al. (Eds.): CMMR 2010, LNCS 6684, pp. 219–241, 2011. c Springer-Verlag Berlin Heidelberg 2011

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Fig. 1. Main diagram model of our system

hand that plucks the strings as the right hand and the hand that presses the frets as the left hand. As a ﬁrst stage of our research, we are developing a tool able to automatically identify, from a recording, the use of guitar articulations. According to Norton [22], guitar articulations can be divided into three main groups related to the place of the sound where they act: attack, sustain, and release articulations. In this research we are focusing on the identiﬁcation of attack articulations such as legatos and glissandos. Speciﬁcally, we present an automatic detection and classiﬁcation system that uses as input audio recordings. We can divide our system into two main modules (Figure 1): extraction and classiﬁcation. The extraction module determines the expressive articulation regions of a classical guitar recording whereas the classiﬁcation module analyzes these regions and determines the king of articulation (legato or glissando). In both, legato and glissando, left hand is involved in the creation of the note onset. In the case of ascending legato, after plucking the string with the right hand, one of the ﬁngers of the l eft hand (not already used for pressing one of the frets), presses a fret causing another note onset. Descending legato is performed by plucking the string with a left-hand ﬁnger that was previously used to play a note (i.e. pressing a fret). The case of glissando is similar but this time after plucking one of the strings with the right hand, the left hand ﬁnger that is pressing the string is slipped to another fret also generating another note onset. When playing legato or glissando on guitar, it is common for the performer to play more notes within a beat than the stated timing enriching the music that is played. A powerful legato and glissando can be diﬀerentiated between each other easily by ear. However, in a musical phrase context where the legato and glissando are not isolated, it is hard to diﬀerentiate among these two expressive articulations. The structure of the paper is as follows: Section 2 brieﬂy describes the current state of the art of guitar analysis studies. Section 3 describes our methodology for articulation determination and classiﬁcation. Section 4 focuses on the experiments conducted to evaluate our approach. Last section, Section 5, summarizes current results and presents the next research steps.

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221

Related Work

Guitar is the one of the most popular instruments in western music. Thus, most of the music genres include the guitar. Although plucked instruments and guitar synthesis have been studied extensively (see [9,22]), the analysis of expressive articulations from real guitar recordings has not been fully tackled. This analysis is complex because guitar is an instrument with a rich repertoire of expressive articulations and because, when playing guitar melodies, several strings may be vibrating at the same time. As an additional statement, even the synthesis of a single tone is a complex subject [9]. Expressive studies go back to the early twentieth century. In 1913, Johnstone [15] analyzed piano performers. Johnstone’s analysis can be considered as one of the ﬁrst studies focusing on musical expressivity. Advances in audio processing techniques risen the opportunity to analyze audio recordings in a ﬁner level (see [12] for an overview). Up to now, there are several studies focused on the analysis of expressivity of diﬀerent instruments. Although the instruments analyzed diﬀer, most of them focus on analyzing monophonic or single instrument recordings. For instance, Mantaras et al [20] presented a survey of computer music systems based on Artiﬁcial Intelligence techniques. Examples of AI-based systems are SaxEx [1] and TempoExpress [13]. Saxex is cased-based reasoning system that generates expressive jazz saxophone melodies from recorded examples of human performances. More recently, TempoExpress performs tempo transformations of audio recordings taking into account the expressive characteristics of a performance and using a CBR approach. Regarding guitar analysis, an interesting research is from Stanford University. Traube [28], estimated the plucking point on a guitar string by using a frequency-domain technique applied to acoustically recorded signals. The plucking point of a guitar string aﬀects the sound envelope and inﬂuences the timbral characteristics of notes. For instance, plucking close to the guitar hole produces more mellow and sustained sounds where plucking near to the bridge (end of the guitar body) produces sharper and less sustained sounds. Traube also proposed an original method to detect the ﬁngering point, based on the plucking point information. In another interesting paper, Lee [17] proposes a new method for extraction of the excitation point of an acoustic guitar signal. Before explaining the method, three state of the art techniques are examined in order to compare with the new one. The techniques analyzed are matrix pencil inverse-ﬁltering, sinusoids plus noise inverse-ﬁltering, and magnitude spectrum smoothing. After describing and comparing these three techniques, the author proposes a new method, statistical spectral interpolation, for excitation signal extraction. Although ﬁngering studies are not directly related with expressivity, their results may contribute to clarify and/or constrain the use of left-hand expressive articulations. Hank Heijink and Ruud G. J. Meulenbroek[14] performed a behavioral study about the complexity of the left hand ﬁngering of classical guitar. Diﬀerent audio and camera recordings of six professional guitarists playing the

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same song were used to ﬁnd optimal places and ﬁngerings for the notes. Several constraints were introduced to calculate cost functions such as; minimization of jerk, torque change, muscle-tension change, work, energy and neuromotor variance. As a result of the study, they found a signiﬁcant eﬀect on timing. On another interesting study, [25] investigates the the optimal ﬁngering position for a given set of notes. Their method, path diﬀerence learning, uses tablatures and AI techniques to obtain ﬁngering positions and transitions. Radicioni et al [24] also worked on ﬁnding the proper ﬁngering position and transitions. Speciﬁcally, they calculated the weights of the ﬁnger transitions between ﬁnger positions by using the weights of Heijing [14]. Burns and Wanderley [4] proposed a method to visually detect and recognize ﬁngering gestures of the left hand of a guitarist by using aﬀordable camera. Unlike the general trend in the literature, Trajano [27] investigated the right hand ﬁngering. Although he analyzed the right-hand, his approach has similarities with left-hand studies. In his article, Trajano uses his own deﬁnitions and cost functions to calculate the optimal selection of right hand ﬁngers. The ﬁrst step when analyzing guitar expressivity is to identify and characterize the way notes are played, i.e. guitar articulations. The analysis of expressive articulations has been previously performed with image analysis techniques. Last but not least, one of the few studies that is focusing on guitar expressivity is the PhD thesis of Norton [22]. In his dissertation Norton proposed the use of a motion caption system based on PhaseSpace Inc, to analyze guitar articulations.

3

Methodology

Articulation refers to how the pieces of something are joined together. In music, these pieces are the notes and the diﬀerent ways of executing them are called articulations. In this paper we propose a new system that is able to determine and classify two expressive articulations from audio ﬁles. For this purpose we have two main modules: the extraction module and the classiﬁcation module (see Figure 1). In the extraction module, we determine the sound segments where expressive articulations are present. The purpose of this module is to classify audio regions as expressive articulations or not. Next, the classiﬁcation module, analyzes the regions that were identiﬁed as candidates of expressive articulations by the extraction module, and label them as legato or glissando. 3.1

Extraction

The goal of the extraction module is to ﬁnd the places where a performer played expressive articulations. To that purpose, we analyzed a recording using several audio analysis algorithms, and combined the information obtained from them to take a decision. Our approach is based on ﬁrst determining the note onsets caused when plucking the strings. Next, a more ﬁne grained analysis is performed inside the regions delimited by two plucking onsets to determine whether an articulation may be present. A simple representation diagram of extraction module is shown in Figure 2.

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Fig. 2. Extraction module diagram

For the analysis we used Aubio [2]. Aubio is a library designed for the annotation of audio signals. Aubio library includes four main applications: abioonset, aubionotes, aubiocut, and aubiopitch. Each application gives us the chance of trying diﬀerent algorithms and also tuning several other parameters. In the current prototype we are using aubioonset for our plucking detection sub-module and aubionotes for our pitch detection sub-module. At the end we combine the outputs from both sub-modules and decide whether there is an expressive articulation or not. In the next two sections, the plucking detection sub-module and the pitch detection sub-module is described. Finally, we explain how we combine the information provided by these two sub-modules to determine the existence of expressive articulations. Plucking Detection. Our ﬁrst task is to determine the onsets caused by the plucking hand. As we stated before, guitar performers can apply diﬀerent articulations by using both of their hands. However, the kind of articulations that we are investigating (legatos and glissandos) are performed by the left hand. Although they can cause onsets, these onsets are not as powerful in terms of both energy and harmonicity [28]. Therefore, we need an onset determination algorithm suitable to this speciﬁc characterictic. The High Frequency Content measure is a measure taken across a signal spectrum, and can be used to characterize the amount of high-frequency content (HFC) in the signal. The magnitudes of the spectral bins are added together, but multiplying each magnitude by the bin position [21]. As Brossier stated, HFC is eﬀective with percussive onsets but less successful determining non-percussive and legato phrases [3]. As right hand onsets are more percussive than left hand onsets, HFC was the strongest candidate of detection algorithm for right hand onsets. HFC is sensitive to abrupt onsets but not too much sensitive for the changes of fundamental frequency caused by the left hand. This is the main reason why we chose HFC to measure the changes on the harmonic content of the signal. Aubioonset library gave us the opportunity to tune the peak-picking threshold, which we tested with a set of hand labeled recordings, including both articulated and non-articulated notes. We used 1.7 for peak picking threshold and

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Fig. 3. HFC onsets

Fig. 4. Features of the portion between two onsets

−95db for silence threshold. We used this set as our ground truth and tuned our values according to this set. An example of the resulting onsets proposed by HFC is shown in Figure 3. Speciﬁcally, in the exempliﬁed recording 5 plucking onsets are detected, onsets caused by the plucking hand, which are shown with vertical lines. Between some of two detected onsets expressive articulations are present. However, as shown in the ﬁgure, HFC succeeds as it only determines the onsets caused by the right hand. Next, each portion between two plucking onsets is analyzed individually. Speciﬁcally, we are interested in determining two points: the end of the attack and the release start. From experimental measures, attack end position is considered 10ms after the amplitude reaches its local maximum. The release start

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Fig. 5. Note Extraction without chroma feature

position is considered as the ﬁnal point where local amplitude is equal or greater than 3 percent of the local maximum. For example, in Figure 4, the ﬁrst portion of the Figure 3 is zoomed. The ﬁrst and the last lines are the plucking onsets identiﬁed by HFC algorithm. The ﬁrst dashed line is the place where attack ﬁnishes. The second dashed line is the place where release starts. Pitch Detection. Our second task was to analyze the sound fragment between two onsets. Since we know the onset values of plucking hand, what we require is another peak detection algorithm with a lower threshold in order to capture the changes in fundamental frequency. Speciﬁcally, if fundamental frequency is not constant between two onsets, we consider that the possibility of the existence of an expressive articulation is high. In the pitch detection module, i.e to extract onsets and their corresponding fundamental frequencies, we used aubionotes. In Aubio library, both onset detection and fundamental frequency estimation algorithms can be chosen from a bunch of alternatives. For onset detection, this time we need a more sensitive algorithm than the one we used to detect the right hand onset detection. Thus, we used complex domain algorithm [8] to determine the peaks and Yin [6] for the fundamental frequency estimation. Complex domain onset detection is based on a combination of phase approach and energy based approach. We used 2048 bins as our window size, 512 bins as our hop size, 1 as our pick peaking threshold and −95db as our silence threshold. With these parameters we obtained an output like the shown in Figure 5. As shown in the ﬁgure, ﬁrst results were not as we expected. Speciﬁcally, they were noisier than expected. There were noisy parts, especially at the beginning of the notes, which generated

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Fig. 6. Note Extraction with chroma feature

false-positive peaks. For instance in Figure 5, many false positive note onsets are detected between the interval from 0 to 0.2 seconds. A careful analysis of the results demonstrated that the false-positive peaks were located in the region of the notes frequency borders. Therefore, we propose a lightweight solution for the problem: to apply a chroma ﬁltering to the regions that are in the borders of Complex domain peaks. As shown in Figure 6, after applying chroma conversion, the results are drastically improved. Next, we analyzed the fragments between two onsets based on the segments provided by the plucking detection module. Speciﬁcally, we analyzed the sound fragment between attack ending point and release starting point (because the noisiest part of a signal is the attack part and the release part of a signal contains unnecessary information for pitch detection [7]). Therefore, for our analysis we take the fragment between attack and release parts where pitch information is relatively constant. Figure 7 shows fundamental frequency values and right hand onsets. X-axis represents the time domain bins and Y-axis represents the frequency. In Figure 7, vertical lines depict the attack and release parts respectively. In the middle there is a change in frequency, which was not determined as an onset by the ﬁrst module. Although it seems like an error, it is a success result for our model. Speciﬁcally, in this phrase there is glissando, which is a left hand articulation, and was not identiﬁed as an onset by plucking detection module (HFC algorithm), but identiﬁed by the pitch detection module (Complex Domain algorithm). The output of the pitch detection module for this recording is shown in Table 1. Analysis and Annotation. After obtaining the results from plucking detection and pitch detection modules, the goal of the analysis and annotation module is to determine the candidates of expressive articulations. Speciﬁcally, from the results of the pitch detection module, we analyze the diﬀerences of fundamental

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Fig. 7. Example of a glissando articulation Table 1. Output of the pitch detection module Note Start (ms.) Fundamental Frequency 0.02 130 0.19 130 0.37 130 0.46 146 0.66 146 0.76 146 099 146 1.10 146 1.41 174 1.48 116

frequencies in the segments between attack and release parts (provided by the plucking detection module). For instance, in Table 1 the light gray values represent the attack and release parts, which we did not take into account while applying our decision algorithm. The diﬀerences of fundamental frequencies are calculated by subtracting to each bin its preceding bin. Thus, when the fragment we are examining is a nonarticulated fragment, this operation returns 0 for all bins. On the other side, in expressively articulated fragments some peaks will arise (see Figure 8 for an example). In Figure 8 there is only one peak, but in other recordings some consecutive peaks may arise. The explanation is that the left hand also causes an onset, i.e. it generates also a transient part. As a result of this transient, more than one change in fundamental frequency may be present. If those changes or peaks are close to each other we consider them as a single peak. We deﬁne this closeness with a pre-determined consecutiveness threshold. Speciﬁcally, if the maximum distance between these peaks is 5 bins, we

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Fig. 8. Diﬀerence vector of pitch frequency values of fundamental frequency array

consider them as an expressive articulation candidate peak. However, if the peaks are separated each other more than the consecutiveness threshold, the fragment is not considered an articulation candidate. Our consideration is that it responds to a probable noisy part of the signal, a crackle in the recording, or a digital conversion error. 3.2

Classification

The classiﬁcation module analyzes the regions identiﬁed by the extraction module and label them as legato or glissando. A diagram of the classiﬁcation module is shown in Figure 9. In this section, ﬁrst, we describe our research to select the appropriate descriptor to analyze the behavior of legato and glissando. Then, we explain the new two components, Models Builder and Detection. Selecting a Descriptor. After extracting the regions which contain candidates of expressive articulations, the next step was to analyze them. Because diﬀerent expressive articulations (legato vs glissando) should present diﬀerent characteristics in terms of changes in amplitude, aperiodicity, or pitch [22], we focused the analysis on comparing these deviations. Speciﬁcally, we built representations of these three features (amplitude, aperiodicity, and pitch). Representations helped us to compare diﬀerent data with diﬀerent length and density. As we stated above, we are mostly interested in changes: changes in High Frequency Content, changes in fundamental frequency, changes in amplitude, etc. Therefore, we explored the peaks in the examined data because peaks are the points where changes occur. As an example, Figure 10 shows, from top to bottom, amplitude evolution, pitch evolution, and changes in aperiodicity for both legato and glissando. As both Figures show, glissando and legato examples, the changes in pitch are similar. However, the changes in amplitude and aperiodicity present a characteristic slope. Thus, as a ﬁrst step we concentrated on determining which descriptor could be used. To make this decision, we built models for both aperiodicty and

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Fig. 9. Classiﬁcation module diagram

(a) Features of a legato example

(b) features of a glissando example

Fig. 10. From top to bottom, representations of amplitude, pitch and aperiodicty of the examined regions

amplitude by using a set of training data. As a result, we obtained two models (for amplitude and aperiodicity) for both legato and glissando as is shown in Figure 11a and Figure 11b. Analyzing the results, amplitude is not a good candidate because the models behave similarly. In contrast, aperiodicity models present a diﬀerent behavior. Therefore, we selected aperiodicity as the descriptor. The details of this model construction will be explained in Building the Models section. Preprocessing. Before analyzing and testing our recordings, we applied two diﬀerent preprocessing techniques to the data in order to make them smoother and ready for comparison: Smoothing and Envelope Approximation. 1. Smoothing. As expected, aperiodicity portion of the audio ﬁle that we are examining includes noise. Our ﬁrst concern was to avoid this noise and obtain a nicer representation. In order to do that ﬁrst we applied a 50 step running median smoothing. Running median smoothing is also known as median ﬁltering. Median ﬁltering is widely used in digital image processing

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(a) Amplitude models

(b) Aperiodicity models

Fig. 11. Models for Legato and Glissando

(a) Aperiodicity

(b) Smoothed Aperiodicity

Fig. 12. Features of aperiodicity

because under certain conditions, it preserves edges whilst removing noise. In our situation since we are interested in the edges and in removing noise, this approach ﬁts our purposes. By smoothing, the peaks locations of the aperiodicity curves become more easy to extract. In Figure 12, comparison of aperiodicity and smoothed aperiodicity graphs exemplify the smoothing process and show the results we pursued. 2. Envelope Approximation. After obtaining a smoother data, an envelope approximation algorithm was applied. The core idea of the envelope approximation is to obtain a ﬁxed length representation of the data, specially considering the peaks and also avoiding small deviations by connecting these peak approximations linearly. The envelope approximation algorithm has three parts: peak peaking, scaling of peak positions according to a ﬁxed length, and linearly connecting the peaks. After the envelope approximation, all data regions we are investigating had the same length, i.e. regions were compressed or enlarged depending on their initial size. We collect all the peaks above a pre-determined threshold. Next, we scale all these peak positions. For instance, imagine that our data includes 10000 bins and we want to scale this data to 1000. And lets say, our peak positions are : 1460, 1465, 1470, 1500 and 1501. What our algorithm does is to scale these peak locations dividing all peak locations by 10 (since we want to scale

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Fig. 13. Envelope approximation of a legato portion

10000 to 1000) and round them. So they become 146, 146, 147, 150 and 150. As shown, we have 2 peaks in 146 and 150. In order to ﬁx this duplicity, we choose the ones with the highest peak value. After collecting and scaling peak positions, the peaks are linearly connected. As shown in Figure 13, the obtained graph is an approximation of the graph shown in Figure 12b. Linear approximation helps the system to avoid consecutive small tips and dips. In our case all the recordings were performed at 60bpm and all the notes in the recordings are 8th notes. That is, each note is half a second, and each legato or glissando portion is 1 second. We recorded with a sampling rate of 44100, and we did our analysis by using a hop size of 32 bins, i.e. 44100/32 = 1378 bins. We knew that this was our highest limit. For the sake of simplicity, we scaled our x-axis to 1000 bins. Building the Models. After applying the preprocessing techniques, we obtained equal length aperiodicity representations of all our expressive articulation portions. Next step was to construct models for both legato and glissando by using this data. In this section we describe how we constructed the models shown in Figure 11a and Figure 11b. The following steps were used to construct the models: Histogram Calculation, Smoothing and Envelope approximation (explained in Preprocessing section), and ﬁnally, SAX representation. In this section we present the Histogram Calculation and the SAX representation techniques. 1. Histogram Calculation. Another method that we are using is histogram envelope calculation. We use this technique to calculate the peak density of a set of data. Speciﬁcally, a set of recordings containing 36 legato and 36 glissando examples (recorded by a professional classical guitarist) was used as training set. First, for each legato and glissando example, we determined the peaks. Since we want to model the places where condensed peaks occur, this

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(a) Legato Histogram

(b) Glisando Histogram

Fig. 14. Peak histograms of legato and glissando training sets

(a) Legato Final Envelope

(b) Glisando Final Envelope

Fig. 15. Final envelope approximation of peak histograms of legato and glissando training sets

time we used a threshold of 30 percent and collect the peaks with amplitude values above this threshold. Notice that the threshold is diﬀerent than the used in envelope approximation. Then, we used histograms to compute the density of the peak locations. Figure 14 shows the resulting histograms. After constructing the histograms, as shown in Figure 14, we used our envelope approximation method to construct the envelopes of legato and glissando histogram models (see Figure 15). 2. SAX: Symbolic Aggregate Approximation. Although the histogram envelope approximations of legato and glissando in Figure 15 are close to our purposes, they still include noisy sections. Rather than these abrupt changes (noises), we are interested in a more general representation reﬂecting the changes more smoothly. SAX (Symbolic Aggregate Approximation) [18], is a symbolic representation used in time series analysis that provides a dimensionality reduction while preserving the properties of the curves. Moreover, SAX representation makes the distance measurements easier. Then, we applied the SAX representation to histogram envelope approximations.

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(a) Legato SAX Representation (b) Glisando SAX Representation Fig. 16. SAX representation of legato and glissando ﬁnal models

As we mentioned in Envelope Approximation, we scaled the x-axis to 1000. We made tests with step sizes of 10 and 5. As we report in the Experiments section, an step size of 5 gave better results. We also tested with step sizes lower than 5, but the performance clearly decreased. Since we are using an step size of 5, each step becomes 100 bins in length. After obtaining the SAX representation of each expressive articulation, we used our distance calculation algorithm which we are going to explain in the next section. Detection. After obtaining the SAX representation of glissando and legato models, we divided them into 2 regions, a ﬁrst region between bins 400 and 600, and a second region between bins 600 and 800 (see Figure 17). For the expressive articulation excerpt, we have the envelope approximation representation with the same length of the SAX representation of ﬁnal models. So, we can compare the regions. For the ﬁnal expressive articulation models (see Figure 16) we took the value for each region and compute the deviation (slope) between these two regions. We performed this computation for both legato and glissando models separately. We also computed the same deviation for each expressive articulation envelope approximation (see Figure 18). But this time, since we do not have SAX representation, for each region we do not have single values. Therefore, for each region we computed the local maxima and took the deviation (slope) of these two local maxima. After obtaining this value, we may compare this deviation vlue with the numbers that we obtained from both ﬁnal models of legato and glissando. If the deviation value is closer to the legato model, the expressive articulation will be labeled as a legato and vice versa.

4

Experiments

The goal of the experiments realized was to test the performance of our model. Since diﬀerent modules have been designed, and they work independently of each other, we tested Extraction and Classiﬁcation modules separately. After applying separate studies, we combined the results to assess the overall performance of the proposed system.

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(a) Legato

(b) Glisando

Fig. 17. Peak occurrence deviation

Fig. 18. Expressive articultion diﬀerence

As it was explained in Section 1, legato and glissando can be played in ascending or descending intervals. Thus, we were interested in studying the results distinguishing among these two movements. Additionally, since in a guitar there are three nylon strings and three metallic strings, we also studied the results taking into account these two sets of strings. 4.1

Recordings

Borrowing from Carlevaro’s guitar exercises [5], we recorded a collection of ascending and descending chromatic scales. Legato and glissando examples were recorded by a professional classical guitar performer. The performer was asked to play chromatic scales in three diﬀerent regions of the guitar fretboard. Specifically, we recorded notes from the ﬁrst 12 frets of the fretboard where each recording concentrated on 4 speciﬁc frets. The basic exercise from the ﬁrst fretboard region is shown in Figure 19.

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Fig. 19. Legato Score in ﬁrst position

(a) Phrase 1

(b) Phrase 2

(d) Phrase 4

(c) Phrase 3

(e) Phrase 5

Fig. 20. Short melodies

Each scale contains 24 ascending and 24 descending notes. Each exercise contains 12 expressive articulations (the ones connected with an arch). Since we repeated the exercise at three diﬀerent positions, we obtained 36 legato and 36 glissando examples. Notice that we also performed recordings with a neutral articulation (neither legatos nor glissandos). We presented all the 72 examples to our system. As a preliminary test with more realistic recordings, we also recorded a small set of 5-6 note phrases. They include diﬀerent articulations in random places (see Figure 20). As shown in Table 3, each phrase includes diﬀerent combinations of expressive articulations varying from 0-2. For instance, Phrase 3 (see Figure 20c) does not have any expressive articulation and Phrase 4 (see Figure 20d) contains the same notes of Phrase 3 but including two expressive articulations: ﬁrst a legato and next an appoggiatura. 4.2

Experiments with Extraction Module

First, we analyzed the accuracy of the extraction module to identify regions with legatos. The hypothesis was that legatos are the articulations easiest to detect because they are composed of two long notes. Next, we analyzed the accuracy to identify regions with glissandos. Because in this situation the ﬁrst note (the glissando) has a short duration, it may be confused with the attack. Scales. We ﬁrst applied our system to single expressive and non-expressive articulations. All the recordings were hand labeled; they were also our ground

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Nylon String 90% 80% 90% 70% 70%

Metalic String 90% 90% 70% 70% 70%

Table 3. Results of extraction module applied to short phrases Excerpt Name Ground Truth Detected Phrase 1 1 2 Phrase 2 2 2 Phrase 3 0 0 Phrase 4 2 3 Phrase 5 1 1

truth. We compared the output results with annotations. The output was the number of determined expressive articulations in the sound fragment. Analyzing the experiments (see Table 2), diﬀerent conclusions can be extracted. First, as expected, legatos are easier to detect than glissandos. Second, in non-steel strings the melodic direction does not cause a diﬀerent performance. Regarding steel strings, descending legatos are more diﬃcult to detect than ascending legatos (90% versus 70%). This result is not surprising because the plucking action of left hand ﬁngers in descending legatos is slightly similar to a right hand plucking. However, this diﬀerence does not appear in glissandos because the ﬁnger movement is the same. Short melodies. We tested the performance of the extraction model to analyze the recordings of short melodies with the same settings used with scales except for the release threshold. Speciﬁcally, since in short phrase recordings the transition parts between two notes have more noise, the average value of the amplitude between two onsets was higher. Because of that, the release threshold in a more realistic scenario has to be increased. Speciﬁcally, after some experiments, we ﬁxed the release threshold to 30%. Analyzing the results, the performance of our model was similar to the previous experiments, i.e. when we analyze single articulations. However, in two phrases where a note was played with a soft right-hand plucking, these notes were proposed as legato candidates (Phrase 1 and Phrase 4 ). The ﬁnal step of the extraction model is to annotate the sound fragments where a possible attack articulation (legato or glissando) is detected. Speciﬁcally, to help the system’s validation, the whole recording is presented to the user and the candidate fragments to expressive articulations are colored. As example, Figure 21 shows the annotation of Phrase 2 (see score in Figure 20b). Phrase 2 has two expressive articulations that correspond with the portions colored in black.

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Fig. 21. Annotated output of Phrase 2

4.3

Experiments with Classification Module

After testing the Extraction Module, we tested the same audio ﬁles (this time using only the legato and glissando examples) to test our Classiﬁcation Module. As explained in Section 3.2, we performed experiments applying diﬀerent step sizes for the SAX representation. Speciﬁcally (see results reported in Table 4), we may observe that a step size of 5 is the most appropriate setting. This result corroborates that a higher resolution when discretizing is not required and demonstrates that the SAX representation provides a powerful technique to summarize the information about changes. The overall performance for legato identiﬁcation was 83.3% and the overall performance for glissando identiﬁcation was 80.5%. Notice that identiﬁcation of ascending legato reached a 100% of accuracy whereas descending legato achieved only a 66.6%. Regarding glissando, there was no signiﬁcant diﬀerence between ascending or descending accuracy (83.3% versus 77.7%). Finally, analyzing the results when considering the string type, the results presented a similar accuracy on both nylon and metallic strings. 4.4

Experiments with the whole system

After testing the main modules separately, we studied the performance of the whole system using the same recordings. From our previous experiments, an step size of 5 gave the best analyzes results, therefore we run these experiments with only an step size of 5. Since we had errors both in the extraction module and classiﬁcation module, the combined results presented a lower accuracy (see results on Table 5).

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Table 4. Performance of classiﬁcation module applied to test set Step Size Recordings 5 10 Ascending Legato 100.0 % 100.0 % Descending Legato 66.6 % 72.2 % Ascending Glissando 83.3 % 61.1 % Descending Glissando 77.7 % 77.7 % Legato Nylon Strings 80.0 % 86.6 % Legato Metallic Strings 86.6 % 85.6 % Glissando Nylon Strings 83.3 % 61.1 % Glissando Metallic Strings 77.7 % 77.7 %

Table 5. Performance of our whole model applied to test set Recordings Accuracy Ascending Legato 85.0 % Descending Legato 53.6 % Ascending Glissando 58.3 % Descending Glissando 54.4% Legato Nylon Strings 68.0 % Legato Metallic Strings 69.3 % Glissando Nylon Strings 58.3 % Glissando Metallic Strings 54.4 %

In Ascending Legatos we had a 100% of accuracy in the classiﬁcation module experiments (see Table 4), but since there was a 15% total error in detecting ascending legato candidates from the classiﬁcation module (see Table 2), the overall accuracy results decrease to 85% (see Table 5). Also regarding the ascending Glissandos, although we reached a high accuracy in the classiﬁcation module (83.3%), because of having 70% accuracy in the extraction module, the overall result decreased to 58.3%. Similar conclusions can be extracted for rest of the accuracy results.

5

Conclusions

In this paper we presented a system that combines several state of the art analysis algorithms to identify left hand articulations such as legatos and glissandos. Speciﬁcally, our proposal uses HFC for plucking detection and Complex Domain and YIN algorithms for pitch detection. Then, combining the data coming from these diﬀerent sources, we developed a ﬁrst decision mechanism, the Extraction module, to identify regions where attack articulations may be present. Next, the Classiﬁcation module analyzes the regions annotated by the extraction Module and tries to determine the articulation type. Our proposal is to use aperiodicity

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information to identify the articulation and a SAX representation to characterize articulation models. Finally, applying a distance measure to the trained models, articulation candidates are classiﬁed as legato or glissando. We reported experiments to validate our proposal by analyzing a collection of chromatic exercises and short melodies recorded by a professional guitarist. Although we are aware that our current system may be improved, the results showed that it is able to identify and classify successfully these two attackbased articulations. As expected, legato are more easy to identify to glissando. Speciﬁcally, the short duration of a glissando is sometimes confused as a single note attack. As a next step, we plan to incorporate more analysis and decision components into our system with the aim of covering all the main expressive articulations used in guitar playing. We are currently working in improving the performance of both modules and also adding additional expressive resources such as vibrato analysis. Additionally, we are exploring the possibility of dynamically changing the parameters of the analysis algorithms like, for instance, using diﬀerent parameters depending on the string where notes are played.

Acknowledgments This work was partially funded by NEXT-CBR (TIN2009-13692-C03-01), IL4LTS (CSIC-200450E557) and by the Generalitat de Catalunya under the grant 2009¨ SGR-1434. Tan Hakan Ozaslan is a Phd student of the Doctoral Program in Information, Communication, and Audiovisuals Technologies of the Universitat Pompeu Fabra. We also want to thank the professional guitarist Mehmet Ali Yıldırım for his contribution with the recordings.

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Comparing Approaches to the Similarity of Musical Chord Sequences W.B. de Haas1 , Matthias Robine2 , Pierre Hanna2 , Remco C. Veltkamp1 , and Frans Wiering1 1

Utrecht University, Department of Information and Computing Sciences PO Box 80.089, 3508 TB Utrecht, The Netherlands {bas.dehaas,remco.veltkamp,frans.wiering}@cs.uu.nl 2 LaBRI - Universit´e de Bordeaux F-33405 Talence cedex, France {pierre.hanna,matthias.robine}@labri.fr

Abstract. We present a comparison between two recent approaches to the harmonic similarity of musical chords sequences. In contrast to earlier work that mainly focuses on the similarity of musical notation or musical audio, in this paper we speciﬁcally use on the symbolic chord description as the primary musical representation. For an experiment, a large chord sequence corpus was created. In this experiment we compare a geometrical and an alignment approach to harmonic similarity, and measure the eﬀects of chord description detail and a priori key information on retrieval performance. The results show that an alignment approach signiﬁcantly outperforms a geometrical approach in most cases, but that the geometrical approach is computationally more eﬃcient than the alignment approach. Furthermore, the results demonstrate that a priori key information boosts retrieval performance, and that using a triadic chord representation yields signiﬁcantly better results than a simpler or more complex chord representation. Keywords: Music Information Retrieval, Musical Harmony, Similarity, Chord Description, Evaluation, Ground-truth Data.

1

Introduction

In the last decades Music Information Retrieval (MIR) has evolved into a broad research area that aims at making large repositories of digital music maintainable and accessible. Within MIR research two main directions can be discerned: symbolic music retrieval and the retrieval of musical audio. The ﬁrst direction traditionally uses score-based representations to research typical retrieval problems. One of the most important and most intensively studied of these is probably the problem of determining the similarity of a speciﬁc musical feature, e.g. melody, rhythm, etc. The second direction–musical audio retrieval–extracts features from the audio signal and uses these features for estimating whether two pieces of music share certain musical properties. In this paper we focus on a S. Ystad et al. (Eds.): CMMR 2010, LNCS 6684, pp. 242–258, 2011. c Springer-Verlag Berlin Heidelberg 2011

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musical representation that is symbolic but can be automatically derived from musical audio with reasonable eﬀectiveness: chord descriptions. Only recently, partly motivated by the growing interest in audio chord ﬁnding, MIR researchers have started using chords descriptions as principal representation for modeling music similarity. Naturally, these representations are speciﬁcally suitable for capturing the harmonic similarity of a musical piece. However, determining the harmonic similarity of sequences of chords descriptions gives rise to three questions. First, what is harmonic similarity? Second, why do we need harmonic similarity? Last, do sequences of chord descriptions provide a valid and useful abstraction of the musical data for determining music similarity? The ﬁrst two questions we will address in this introduction; the third question we will answer empirically in a retrieval experiment. In this experiment we will compare a geometrical and an alignment based harmonic similarity measure. The ﬁrst question–what is harmonic similarity–is diﬃcult to answer. We strongly believe that if we want to model what makes two pieces of music similar, we must not only look at the musical data, but especially at the human listener. It is important to realize that music only becomes music in the mind of the listener, and probably not all information needed for good similarity judgment can be found in the data alone. Human listeners, musician or non-musician, have extensive culturedependent knowledge about music that needs to be taken into account when judging music similarity. In this light we consider the harmonic similarity of two chord sequences to be the degree of agreement between structures of simultaneously sounding notes (i.e. chords) and the agreement between global as well as local relations between these structures in both sequences as perceived by the human listener. With the agreement between structures of simultaneously sounding notes we denote the similarity that a listener perceives when comparing two chords in isolation and without the surrounding musical context. However, chords are rarely compared in isolation and the relations between the global context–the key–of a piece and the relations to the local context play a very important role in the perception of tonal harmony. The local relations can be considered the relations between functions of chords within a limited time frame, for instance the preparation of a chord with a dominant function with a sub-dominant. All these factors play a role in the perception of tonal harmony and should be shared by two compared pieces up to certain extent to if they are considered similar. The second question about the usefulness of harmonic similarity is easier to answer, since music retrieval based on harmony sequences oﬀers various beneﬁts. It allows for ﬁnding diﬀerent versions of the same song even when melodies vary. This is often the case in cover songs or live performances, especially when these performances contain improvisations. Moreover, playing the same harmony with diﬀerent melodies is an essential part of musical styles like jazz and blues. Also, variations over standard basses in baroque instrumental music can be harmonically very related. The application of harmony matching methods is broadened further by the extensive work on chord description extraction from musical audio data within

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the MIR community, e.g. [20,5]. Chord labeling algorithms extract symbolic chord labels from musical audio: these labels can be matched directly using the algorithms covered in this paper. If you would ask a jazz musician to answer the third question–whether sequences of chord descriptions are useful–he will probably agree that they are, since working with chord descriptions is everyday practice in jazz. However, we will show in this paper that they are also useful for retrieving pieces with a similar but not identical chord sequence by performing a large experiment. In this experiment we compare two harmonic similarity measures, the Tonal Pitch Step Distance (TPSD) [11] and the Chord Sequence Alignment System (CSAS) [12], and test the inﬂuence of diﬀerent degrees of detail in the chord description and the knowledge of the global key of a piece on retrieval performance. The next section gives a brief overview of the current achievements in chord sequence similarity matching and harmonic similarity in general, Section 3 describes the data used in the experiment and Section 4 presents the results. Contribution. This paper presents an overview of chord sequence based harmonic similarity. Two harmonic similarity approaches are compared in an experiment. For this experiment a new large corpus of 5028 chord sequences was assembled. Six retrieval tasks are deﬁned for this corpus, to which both algorithms are subjected. All tasks use the same dataset, but diﬀer in the amount of chord description detail and in the use of a priori key information. The results show that a computational costly alignment approach signiﬁcantly outperforms a much faster geometrical approach in most cases, that a priori key information boosts retrieval performance, and that using a triadic chord representation yields signiﬁcantly better results than a simpler or more complex chord representation.

2

Background: Similarity Measures for Chord Sequences

The harmonic similarity of musical information has been investigated by many authors, but the number of systems that focus solely on similarity chords sequences is much smaller. Of course it is always possible to convert notes into chords and vice versa, but this is not a trivial task. Several algorithms can correctly segment and label approximately 80 percent of a symbolic dataset (see for a review [26]). Within the audio domain hidden Markov Models are frequently used for chord label assignment, e.g. [20,5]. The algorithms considered in this paper abstract from these labeling tasks and focus on the similarity between chord progressions only. As a consequence, we assume that we have a sequence of symbolic chord labels describing the chord progression in a piece of music. The systems currently known to us that are designed to match these sequences of symbolic chord descriptions are the TPSD [11], the CSAS [12] and a harmony grammar approach [10]. The ﬁrst two are quantitatively compared in this paper and are introduced in the next two subsections, respectively. They have been compared before, but all previous evaluations of TPSD and CSAS were done with relatively small datasets (<600 songs) and the dataset used in [12] was

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TPS Score

All The Things You Are 13 12 11 10 9 8 7 6 5 4 3 2 1 0 0

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Fig. 1. A plot demonstrating the comparison of two similar versions of All the Things You Are using the TPSD. The total area between the two step functions, normalized by the duration of the shortest song, represents the distance between both songs. A minimal area is obtained by shifting one of the step functions cyclically.

diﬀerent from the one used in [11]. The harmony grammar approach could, at the time of writing, not compete in this experiment because in its current state it is yet unable to parse all the songs in the used dataset. The next section introduces the TPSD and the improvements over the implementation used for the experiment here and the implementation in [11]. Section 2.2 highlights the diﬀerent variants of the CSAS. The main focus of this paper is on the similarity of sequences of chord labels, but there exist other relevant harmony based retrieval methods: some of these are brieﬂy reviewed in Section 2.3. 2.1

Tonal Pitch Step Distance

The TPSD uses Lerdahl’s [17] Tonal Pitch Space (TPS) as its main musical model. TPS is a model of tonality that ﬁts musicological intuitions, correlates well with empirical ﬁndings from music cognition [16] and can be used to calculate a distance between two arbitrary chords. The TPS model can be seen as a scoring mechanism that takes into account the number of steps on the circle of ﬁfths between the roots of the chords, and the amount of overlap between the chord structures of the two chords and their relation to the global key. The general idea behind the TPSD is to use the TPS to compare the change of chordal distance to the tonic over time. For every chord the TPS distance between the chord and the key of the sequence is calculated, which results in a step function (see Figure 1). As a consequence, information about the key of the piece is essential. Next, the distance between two chord sequences is deﬁned as the minimal area between the two step functions over all possible horizontal circular shifts. To prevent that longer sequences yield larger distances, the score is normalized by dividing it by the duration of the shortest song.

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The TPS is an elaborate model that allows to compare every arbitrary chord in an arbitrary key to every other possible chord in any key. The TPSD does not use the complete model and only utilizes the parts that facilitate the comparison of two chords within the same key. In the current implementation of the TPSD time is represented in beats, but generally any discrete representation could be used. The TPSD version used in this paper contains a few improvements compared to the version used in [11]: by applying a diﬀerent step function matching algorithm from [4], and by exploiting the fact that we use discrete time units that enable us to sort in linear time using counting sort [6], a running time of O(nm) is achieved where n and m are the number of chord symbols in both songs. Furthermore, to be able to use the TPSD in situations where a priori key information is not available, the TPSD is extended with a key ﬁnding algorithm. Key finding. The problem of ﬁnding the global key of piece of music is called key ﬁnding. For this study, this is done on the basis of chord information only. The rationale behind the key ﬁnding algorithm that we present here is the following: we consider the key that minimizes the total TPS distance and best matches the starting and ending chord, the key of the piece. For minimizing the total TPS distance, the TPSD key ﬁnding uses TPS based step functions as well. We assume that when a song matches a particular key, the TPS distances between the chords and the tonic of the key are relatively small. The general idea is to calculate 24 step functions for a single chord sequence, one for each major and minor key. Subsequently, all these keys are ranked by sorting them on the area between their TPS step function and the x-axis; the smaller the total area, the better this key ﬁts the piece, and the higher the rank. Often, the key at the top rank is the correct key. However, among the false positives at rank one, non-surprisingly, the IV, V and VI relative to the ground-truth key1 are found regularly. This makes sense because, when the total of TPS distances of the chords to C is small, the distances to F, G and Am might be small as well. Therefore, to increase performance, an additional scoring mechanism is designed that takes into account the IV, V and VI relative to the ground-truth key. Of all 24 keys, the candidate key that minimizes the following sum S is considered the key of the piece. S = αr(I) + r(IV ) + r(V ) + r(V I) +

β if the ﬁrst chord matches the key (1) β if the last chord matches the key

Here r(.) denotes the rank of the candidate key, a parameter α determines how important the tonic is compared to other frequently occurring scale degrees and β controls the importance of the key matching ﬁrst and last chord. The parameters α and β were tuned by hand and an α of 2, and a β of 4 were found to give good results. Clearly, this simple key-ﬁnding algorithm is biased 1

The roman numbers here represent the diatonic interval between the key in the ground-truth and the predicted key.

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towards western diatonic music, but for the corpus used in this paper it performs quite well. The algorithm scores 88.8 percent correct on a subset of 500 songs of the corpus used in the experiment below for which we manually checked the correctness of the ground-truth key. The above algorithm takes O(n) time, where n is the number of chord symbols, because the number of keys is constant. Root interval step functions. For the tasks where only the chord root is used we use a diﬀerent step function representation (See Section 4). In these tasks the interval between the chord root and the root note of the key deﬁnes the step height and the duration of the chord again deﬁnes the step length. This matching method is very similar to the melody matching approach by Aloupis et al. [2]. Note that the latter was never tested in practice. The matching and key ﬁnding methods are not diﬀerent from the other variants of the TPSD. Note that in all TPSD variants, chord inversions are ignored. 2.2

Chord Sequence Alignment System

The CSAS algorithm is based on local alignment and computes similarity scores between sequences of symbols representing chords or distances between chords and key. String matching techniques can be used to quantify the diﬀerences between two such sequences. Among several existing methods, Smith and Waterman’s approach [25] detects similar areas in two sequences of abitrary symbols. This local alignment or local similarity algorithm locates and extracts a pair of regions, one from each of the two given strings, that exhibit high similarity. A similarity score is calculated by performing elementary operations transforming the one string into the other. The operations used to transform the sequences are deletion, insertion or substitution of a symbol. The total transformation from the one string into the other can be solved with a dynamic programming in quadratic time. The following example illustrates local alignment by computing a distance between the ﬁrst chords of two variants of the song All The Things You Are considering only the root notes of the chords. The I, S and D denote Insertion, Substitution, and Deletion of a symbol, respectively. An M represents a matching symbol: string 1 F B E A D D G C C string 2 F F B A A A D D C C operation I M M S M I M M D M M score −1 +2 +2 −2 +2 −1 +2 +2 −1 +2 +2 Algorithms based on local alignment have been successfully adapted for melodic similarity [21,13,15] and recently it has been used to determine harmonic similarity [12] as well. Two steps are necessary to apply the alignment technique to the comparison of chord progressions: the choice of the representation of a chord sequence, and the scores of the elementary operations between symbols. To take the durations of the chords into account, we represent the chords at every beat. The algorithm has therefore a complexity of O(nm), where n and m are the sizes of

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the compared songs in beats. The score function can either be adapted to the chosen representation or can simply be binary, i.e. the score is positive (+2) if the two chords described are identical, and negative (−2) otherwise. The insertion or deletion score is set to −1. Absolute representation. One way of representing a chord sequence is to simply represent the chord progression as a sequence of absolute root notes and in that case prior knowledge of the key is not required. An absolute representation of the chord progression of the 8 ﬁrst bars of the song All The Things You Are is then: F, B, E, A, D, D, G, C, C In this case, the substitution scores may be determined by considering the diﬀerence in semitones, the number of steps on the circle of ﬁfths between the roots, or by the consonance of the interval between the roots, as described in [13]. For instance, the cost of substituting a C with a G (ﬁfth) is lower than the substitution of a C with a D (Second). Taking into account the mode in the representation can aﬀect the score function as well: a substitution of a C for a Dm is diﬀerent from a substitution of a C for a D, for example. If the two modes are identical, one may slightly increase the similarity score, and decrease it otherwise. Another possible representation of the chord progression is a sequence of absolute pitch sets. In that case one can use musical distances between chords, like Lerdahl’s TPS model [17] or the distance introduced by Paiement et al. [22], as a score function for substitution. Key-relative representation. If key information is known beforehand, a chord can be represented as a distance to this key. The distance can be expressed in various ways: in semitones, or as the number of steps on the circle of ﬁfths between the root of the chord and the tonic of the key of the song, or with more complex musical models, such as TPS. If in this case the key is A and the chord is represented by the diﬀerence in semitones, the representation of the chord progression of the ﬁrst eight bars of the song All The Things You Are will be: 3, 2, 5, 0, 5, 6, 1, 4, 4 If all the notes of the chords are taken into account, the TPS or Paiement distances can be used between the chords and the triad of the key to construct the representation. The representation is then a sequence of distances, and we use an alignment between these distances instead of between the chords themselves. This representation is very similar to the representation used in the TPSD. The score functions used to compare the resulting sequences can then be binary, or linear in similarity regarding the diﬀerence observed in the values. Transposition invariance. In order to be robust to key changes, two identical chord progressions transposed in diﬀerent keys have to be considered as similar. The usual way to deal with this issue [27] is to choose a chord representation which is transposition invariant. A ﬁrst option is to represent transitions

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between successive chords, but this has been proven to be less accurate when applied to alignment algorithms [13]. Another option is to consider a key relative representation, like the representation described above which is by deﬁnition transposition invariant. However, this approach is not robust against local key changes. With an absolute representation of chords, we use an adaptation of the local alignment algorithm proposed in [1]. It allows to take into account an unlimited number of local transpositions and can be applied to representations of chord progressions to account for modulations. According to the choice of the representation and the score function, several variants are possible in order to build an algorithm for harmonic similarity. In Section 4 we explain the diﬀerent representations and scoring functions used in the diﬀerent tasks of the experiment and their eﬀects on retrieval performance. 2.3

Other Methods for Harmonic Similarity

The third harmonic similarity measure using chord descriptions is a generative grammar approach [10]. The authors use a generative grammar of tonal harmony to parse the chord sequences, which results in parse trees that represent harmonic analyses of these sequences. Subsequently, a tree that contains all the information shared by the two parse trees of two compared songs is constructed and several properties of this tree can be analyzed yielding several similarity measures. Currently a parser can reject a sequence of chords as being ungrammatical. Another interesting retrieval system based on harmonic similarity is the one developed by Pickens and Crawford [23]. Instead of describing a musical segment with one chord, they represent a musical segment as a vector describing the ‘ﬁt’ between the segment and every major and minor triad. This system then uses a Markov model to model the transition distributions between these vectors for every piece. Subsequently, these Markov models are ranked using the KullbackLeibler divergence. It would be interesting to compare the performance of these systems to the algorithms tested in here in the future. Other interesting work has been done by Paiement et al. [22]. They deﬁne a similarity measure for chords rather than for chord sequences. Their similarity measure is based on the sum of the perceived strengths of the harmonics of the pitch classes in a chord, resulting in a vector of twelve pitch classes for each musical segment. Paiement et al. subsequently deﬁne the distance between two chords as the euclidean distance between two of these vectors that correspond to the chords. Next, they use a graphical model to model the hierarchical dependencies within a chord progression. In this model they used their chord similarity measure for the calculation of the substitution probabilities between chords.

3

A Chord Sequence Corpus

The Chord Sequence Corpus used in the experiment consists of 5,028 unique human-generated Band-in-a-Box ﬁles that are collected from the Internet. Bandin-a-Box is a commercial software package [9] that is used to generate musical

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Table 1. A leadsheet of the song All The Things You Are. A dot represents a beat, a bar represents a bar line, and the chord labels are presented as written in the Bandin-a-Box ﬁle. |Fm7 . . . |Bbm7 . . . |Eb7 . . . |DbMaj7 . . . |Dm7b5 . G7b9 . |CMaj7 . . . |Cm7 . . . |Fm7 . . . |Bb7 . . . |AbMaj7 . . . |Am7b5 . D7b9 . |GMaj7 . . . |A7 . . . |D7 . . . |GMaj7 . . . |Gbm7 . . . |B7 . . . |EMaj7 . . . |Fm7 . . . |Bbm7 . . . |Eb7 . . . |DbMaj7 . . . |Dbm7 . Gb7 . |Cm7 . . . |Bbm7 . . . |Eb7 . . . |AbMaj7 . . .

|AbMaj7 . . . |CMaj7 . . . |Eb7 . . . |GMaj7 . . . |GMaj7 . . . |C+ . . . |AbMaj7 . . . |Bdim . . . |. . . .

| | | | | | | | |

accompaniment based on a lead sheet. A Band-in-a-Box ﬁle stores a sequence of chords and a certain style, whereupon the program synthesizes and plays a MIDI-based accompaniment. A Band-in-a-Box ﬁle therefore contains a sequence of chords, a melody, a style description, a key description, and some information about the form of the piece, i.e. the number of repetitions, intro, outro etc. For extracting the chord label information from the Band-in-a-Box ﬁles we have extended software developed by Simon Dixon and Matthias Mauch [19]. Throughout this paper we have been referring to chord labels or chord descriptions. To rule out any possible vagueness, we adopt the following deﬁnition of a chord: a chord always consist of a root, a chord type and an optional inversion. The root note is the fundamental note upon which the chord is built, usually as a series of ascending thirds. The chord type (or quality) is the set of intervals relative to the root that make up the chord and the inversion is deﬁned as the degree of the chord that is played as bass note. One of the most distinctive features of the chord type is its mode, which can either be major or minor. Although a chord label always describes these three properties, root, chord type and inversion, musicians and researchers use diﬀerent syntactical systems to describe them, and also Band-in-a-Box uses its own syntax to represent the chords. Harte et al. [14] give an in depth overview of the problems related to representing chords and suggests a unambiguous syntax for chord labels. An example of a chord sequence as found in a Band-in-a-Box ﬁle describing the chord sequence of All the Things You Are is given in Table 1. All songs of the chord sequence corpus were collected from various Internet sources. These songs were labeled and automatically checked for having a unique chord sequence. All chord sequences describe complete songs and songs with fewer than 3 chords or shorter than 16 beats were removed from the corpus in an earlier stage. The titles of the songs, which function as a ground-truth, as well as the correctness of the key assignments, were checked and corrected manually. The style of the songs is mainly jazz, latin and pop. Within the collection, 1775 songs contain two or more similar versions, forming 691 classes of songs. Within a song class, songs have the same title and share a similar melody, but may diﬀer in a number of ways. They may, for instance, diﬀer in key and form, they may diﬀer in the number of repetitions, or have a

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Table 2. The distribution of the song class sizes in the Chord Sequence Corpus Class Size 1 2 3 4 5 6 7 8 10 Total

Frequency 3,253 452 137 67 25 7 1 1 1 5028

Percent 82.50 11.46 3.47 1.70 .63 .18 .03 .03 .03 100

special introduction or ending. The richness of the chords descriptions may also diverge, i.e. a C7913 may be written instead of a C7 , and common substitutions frequently occur. Examples of the latter are relative substitution, i.e. Am instead of C, or tritone substitution, e.g. F#7 instead of C7 . Having multiple chord sequences describing the same song allows for setting up a cover-song ﬁnding experiment. The the title of the song is used as ground-truth and the retrieval challenge is to ﬁnd the other chord sequences representing the same song. The distribution of the song class sizes is displayed in Table 2 and gives an impression of the diﬃculty of the retrieval task. Generally, Table 2 shows that the song classes are relatively small and that for the majority of the queries there is only one relevant document to be found. It furthermore shows that 82.5% of the songs is in the corpus for distraction only. The chord sequence corpus is available to the research community on request.

4

Experiment: Comparing Retrieval Performance

We compared the TPSD and the CSAS in six retrieval tasks. For this experiment we used the chord sequence corpus described above, which contains sequences that clearly describe the same song. For each of these tasks the experimental setup was identical: all songs that have two or more similar versions were used as a query, yielding 1775 queries. For each query a ranking was created by sorting the other songs on their TPSD and CSAS scores and these rankings and the runtimes of the compared algorithms were analyzed. 4.1

Tasks

The tasks, summarized in Table 3, diﬀered in the level of chord information used by the algorithms and in the usage of a priori global key information. In tasks 1-3 no key information was presented to the algorithms and in the remaining three tasks we used the key information, which was manually checked for correctness, as stored in the Band-in-a-Box ﬁles. The tasks 1-3 and 4-6 furthermore diﬀered in the amount of chord detail that was presented to the algorithms: in tasks 1

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Key Key Key Key Key Key Key

Information inferred inferred inferred as stored in the Band-in-a-Box ﬁle as stored in the Band-in-a-Box ﬁle as stored in the Band-in-a-Box ﬁle

and 4 only the root note of the chord was available to the algorithms, in tasks 2 and 5 the root and the triad were available and in tasks 3 and 6 the complete chord as stored in the Band-in-a-Box ﬁle was presented to the algorithms. The diﬀerent tasks required speciﬁc variants of the tested algorithms. For tasks 1-3 the TPSD used the TPS key ﬁnding algorithm as described in Section 2.1. For the tasks 1 and 4, involving only chord roots, a simpliﬁed variant of the TPSD was used, for the tasks 2, 3, 5 and 6 we used the regular TPSD, as described in Section 2.1 and [11]. To measure the impact of the chord representation and substitution functions on retrieval performance, diﬀerent variants of the CSAS were built also. In some cases the choices made did not yield the best possible results, but they allow the reader to understand the eﬀects of the parameters used on retrieval performance. The CSAS algorithms in tasks 1-3 all used an absolute representation and the algorithms in tasks 4-6 used a key relative representation. In tasks 4 and 5 the chords were represented as the diﬀerence in semitones to the root of the key of the piece and in task 6 as the Lerdahl’s TPS distance between the chord and the triad from the key (as in the TPSD). The CSAS variants in tasks 1 and 2 used a consonance based substitution function and algorithms in tasks 4-6 a binary substitution function was used. In tasks 2 and 5 a binary substitution function for the mode was used as well: if the mode of the substituted chords matched, no penalty was given, if they did not match, a penalty was given. A last parameter that was varied was the use of local transpositions. The CSAS variants applied in tasks 1 and 3 did not consider local transpositions, but the CSAS algorithm used in task 2 did allow local transpositions (see Section 2.2 for details). The TPSD was implemented in Java and the CSAS was implemented in C++, but a small Java program was used to parallelize the matching process. All runs were done on a Intel Xeon quad-core CPU at a frequency of 1.86 GHz. and 4 Gb of RAM running 32 bit Linux. Both algorithms were parallelized to optimally use the multiple cores of the CPUs. 4.2

Results

For each task and for each algorithm we analyzed the rankings of all 1775 queries with 11-point precision recall curves and Mean Average Precision (MAP). Figure 2 displays the interpolated average precision and recall chart for the TPSD and the CSAS for all tasks listed in Table 3. We calculated the interpolated average

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precision as in [18] and probed it at 11 diﬀerent recall levels. In all evaluations the query was excluded from the analyzed rankings. In tasks 2 and 4-6 the CSAS outperforms the TPSD and in tasks 1 and 3 the TPSD outperforms the CSAS. The curves all have a very similar shape, this is probably due to the speciﬁc sizes of the song classes and the fairly limited amount of large song classes (see Table 2). In Figure 3 we present the MAP and the runtimes of the algorithms on two diﬀerent axes. The MAP is displayed on the left axis and the runtimes are shown on right axis that has an exponential scale doubling the amount of time at every tick. The MAP is a single-ﬁgure measure, which measures the precision at all recall levels and approximates the area under the (uninterpolated) precision recall graph [18]. Having a single measure of retrieval quality makes it easier to evaluate the signiﬁcance of the diﬀerences between results. We tested whether there were signiﬁcant diﬀerences in MAP by performing a non-parametric Friedman test, with a signiﬁcance level of α = .05. We chose the Friedman test because the underlying distribution of the data is unknown and in contrast to an ANOVA the Friedman does not assume a speciﬁc distribution of variance. There were signiﬁcant diﬀerences between the 12 runs, χ2 (11, N = 1775) = 2, 6182, p < .0001. To determine which of the pairs of measurements diﬀered signiﬁcantly we conducted a post hoc Tukey HSD test3 . Other than a regular T-test, the Tukey HSD test can be safely used for comparing multiple means [7]. A summary of the analyzed conﬁdence intervals is given in Table 4. Signiﬁcant and non-signiﬁcant diﬀerences are denoted with +’s and –’s, respectively. 2 3

The 12 diﬀerent runs introduce 11 degrees of freedom and 1775 individual queries were examined per run. All statistical tests were performed in Matlab 2009a.

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Fig. 3. The MAP and Runtimes of the TPSD and the CSAS. The MAP is displayed on the left axis and the runtimes are displayed on an exponential scale on the right axis. On the left side of the chart the key inferred tasks are displayed and the key relative tasks are displayed on the right side.

The overall retrieval performance of all algorithms on all tasks can be considered good, but there are some large diﬀerences between tasks and between algorithms, both in performance and in runtime. With a MAP of .70 the overall best performing setup was the CSAS using triadic chord descriptions and a key relative representation (task 5). The TPSD also performs best on task 5 with an MAP of .58. In tasks 2 and 4-6 the CSAS signiﬁcantly outperforms the TPSD. On tasks 1 and 3 the TPSD outperforms the CSAS in runtime as well as performance. For these two tasks, the results obtained by the CSAS are signiﬁcantly lower because local transpositions are not considered. These results show that taking into account transpositions has a high impact on the quality of the retrieval system, but also on the runtime. The retrieval performance of the CSAS is good, but comes at a price. On average over six of the twelve runs, the CSAS runs need about 136 times as much time to complete as the TPSD. The TPSD takes about 30 minutes to 1.5 hours to match all 5028 pieces, while the CSAS takes about 2 to 9 days. Due to the fact that the CSAS run in task 2 takes 206 hours to complete, there was not enough time to perform a run on task 1 and 3 with the CSAS variant that takes local transpositions into account.

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Table 4. This table shows for each pair of runs if the mean average precision, as displayed in Figure 3 diﬀered signiﬁcantly (+) or not (–) Key Inferred task1 task2 TPSD TPSD key task2 TPSD + inferred task3 TPSD – + task1 CSAS + + task2 CSAS + + task3 CSAS + + key task4 TPSD + – information task5 TPSD + + available task6 TPSD + – task4 CSAS + + task5 CSAS + + task6 CSAS + +

Key Information available task3 task1 task2 task3 task4 task5 task6 task4 task5 TPSD CSAS CSAS CSAS TPSD TPSD TPSD CSAS CSAS

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In task 6 both algorithms represent the chord sequences as TPS distances to the triad of the key. Nevertheless, the TPSD is outperformed by the CSAS. This diﬀerence as well as other diﬀerences in performance might well be explained by the insertion and deletion operations in the CSAS algorithm: if one takes two identical pieces an inserts one arbitrary extra chord somewhere in the middle of the piece, an asynchrony is created between the two step functions which has a large eﬀect on the estimated distance, while the CSAS distance only gains one extra deletion score. For the CSAS algorithm we did a few additional runs that are not reported here. These runs showed that the diﬀerence in retrieval performance using different substitution costs (binary, consonance or semi-tones) is limited. The runs in which a priori key information was available performed better, regardless of the task or algorithm (compare tasks 1 and 4, 2 and 5, and 3 and 6 for both algorithms in Table 4). This was to be expected because there are always errors in the key ﬁnding, which hampers the retrieval performance. The amount of detail in the chord description has a signiﬁcant eﬀect on the retrieval performance of all algorithms. In almost all cases, using only the triadic chord description for retrieval yields better results than using only the root or the complex chord descriptions. Only the diﬀerence in CSAS performance between using complex chords or triads is not signiﬁcant in task 5 and 6. The diﬀerences between using only the root or using the complete chord are smaller and not always signiﬁcant. Thus, although colorful additions to chords may sound pleasant to the human ear, they are not always beneﬁcial for determining the similarity between the harmonic progressions they represent. There might be a simple explanation for these diﬀerences in performance. Using only the root of a chord already leads to good retrieval results, but by removing good information about the mode one looses information that can aid in boosting the retrieval performance.

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On the other hand keeping all rich chord information seems to distract the evaluated retrieval systems. Pruning the chord structure down to the triad might be seen as a form of syntactical noise-reduction, since the chord additions, if they do not have a voice leading function, have a rather arbitrary character and just add some harmonic spice.

5

Concluding Remarks

We performed a comparison of two diﬀerent chord sequence similarity measures, the Tonal Pitch Space Distance (TPSD) and the Chord Sequence Alignment System (CSAS), on a large newly assembled corpus of 5028 symbolic chord sequences. The comparison consisted of six diﬀerent tasks, in which we varied the amount of detail in the chord description and the availability of a priori key information. The CSAS variants outperform the TPSD signiﬁcantly in most cases, but is in all cases far more costly to use. The use of a priori key information improves performance and using only the triad of a chord for similarity matching gives the best results for the tested algorithms. Nevertheless, we can positively answer the third question that we have asked ourselves in the introduction–do chord descriptions provided a useful and valid abstraction–because the experiment presented in the previous section clearly shows that chord descriptions can be used for retrieving harmonically related pieces. The retrieval performance of both algorithms is good, especially if one considers the size of the corpus and the relatively small class sizes (see Table 2), but there is still room for improvement. Both algorithms cannot deal with large structural changes, e.g. adding repetitions, a bridge, etc. A prior analysis of the structure of the piece combined with partial matching could improve the retrieval performance. Another important issue is that the compared systems treat all chords as equally important. This is musicologically not plausible. Considering the musical function in the local as well as global structure of the chord progression, like is done in [10] or with sequences of notes in [24], might still improve the retrieval results. With runtimes that are measured in days, the CSAS is a costly system. The runtimes might be improved by using GPU programming [8], or with ﬁltering steps using algorithms such as BLAST [3]. The harmonic retrieval systems and experiments presented in this paper consider a speciﬁc form of symbolic music representations only. Nevertheless, the application of the methods here presented is not limited to symbolic music and audio applications are currently investigated. Especially the recent developments in chord label extraction are very promising because the output of these methods could be matched directly with the systems here presented. The good performance of the proposed algorithms leads us to believe that, both in the audio and symbolic domain, retrieval systems will beneﬁt from chord sequence based matching in the near future.

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18. Manning, C., Raghavan, P., Sch¨ utze, H.: Introduction to Information Retrieval. Cambridge University Press, New York (2008) 19. Mauch, M., Dixon, S., Harte, C., Casey, M., Fields, B.: Discovering Chord Idioms through Beatles and Real Book Songs. In: Proceedings of the Eighth International Society for Music Information Retrieval Conference (ISMIR), pp. 255–258 (2007) 20. Mauch, M., Noland, K., Dixon, S.: Using Musical Structure to Enhance Automatic Chord Transcription. In: Proceedings of the Tenth International Society for Music Information Retrieval Conference (ISMIR), pp. 231–236 (2009) 21. Mongeau, M., Sankoﬀ, D.: Comparison of Musical Sequences. Computers and the Humanities 24(3), 161–175 (1990) 22. Paiement, J.F., Eck, D., Bengio, S.: A Probabilistic Model for Chord Progressions. In: Proceedings of the Sixth International Conference on Music Information Retrieval (ISMIR), London, UK, pp. 312–319 (2005) 23. Pickens, J., Crawford, T.: Harmonic Models for Polyphonic Music Retrieval. In: Proceedings of the Eleventh International Conference on Information and Knowledge Management, pp. 430–437. ACM, New York (2002) 24. Robine, M., Hanna, P., Ferraro, P.: Music Similarity: Improvements of Edit-based Algorithms by Considering Music Theory. In: Proceedings of the ACM SIGMM International Workshop on Multimedia Information Retrieval (MIR), Augsburg, Germany, pp. 135–141 (2007) 25. Smith, T., Waterman, M.: Identiﬁcation of Common Molecular Subsequences. Journal of Molecular Biology 147, 195–197 (1981) 26. Temperley, D.: The Cognition of Basic Musical Structures. MIT Press, Cambridge (2001) 27. Uitdenbogerd, A.L.: Music Information Retrieval Technology. Ph.D. thesis, RMIT University, Melbourne, Australia (July 2002)

Songs2See and GlobalMusic2One: Two Applied Research Projects in Music Information Retrieval at Fraunhofer IDMT Christian Dittmar, Holger Großmann, Estefan´ıa Cano, Sascha Grollmisch, Hanna Lukashevich, and Jakob Abeßer Fraunhofer IDMT Ehrenbergstr. 31, 98693 Ilmenau, Germany {dmr,grn,cano,goh,lkh,abr}@idmt.fraunhofer.de http://www.idmt.fraunhofer.de

Abstract. At the Fraunhofer Institute for Digital Media Technology (IDMT) in Ilmenau, Germany, two current research projects are directed towards core problems of Music Information Retrieval. The Songs2See project is supported by the Thuringian Ministry of Economy, Employment and Technology through granting funds of the European Fund for Regional Development. The target outcome of this project is a web-based application that assists music students with their instrumental exercises. The unique advantage over existing e-learning solutions is the opportunity to create personalized exercise content using the favorite songs of the music student. GlobalMusic2one is a research project supported by the German Ministry of Education and Research. It is set out to develop a new generation of hybrid music search and recommendation engines. The target outcomes are novel adaptive methods of Music Information Retrieval in combination with Web 2.0 technologies for better quality in the automated recommendation and online marketing of world music collections. Keywords: music information retrieval, automatic music transcription, music source separation, automatic music annotation, music similarity search, music education games.

1

Introduction

Successful exploitation of results from basic research is the indicator for the practical relevance of a research ﬁeld. During recent years, the scientiﬁc and commercial interest in the comparatively young research discipline called Music Information Retrieval (MIR) has grown considerably. Stimulated by the evergrowing availability and size of digital music catalogs and mobile media players, MIR techniques become increasingly important to aid convenient exploration of large music collections (e.g., through recommendation engines) and to enable entirely new forms of music consumption (e.g., through music games). Evidently, commercial entities like online music shops, record labels and content aggregators S. Ystad et al. (Eds.): CMMR 2010, LNCS 6684, pp. 259–272, 2011. c Springer-Verlag Berlin Heidelberg 2011

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have realized that these aspects can make them stand out among their competitors and foster customer loyalty. However, the industry’s willingness to fund basic research in MIR is comparatively low. Thus, only well described methods have found successful application in the real world. For music recommendation and retrieval, these are doubtlessly services based on collaborative ﬁltering1 (CF). For music transcription and interaction, these are successful video game titles using monophonic pitch detection2 . The two research projects in the scope of this paper provide the opportunity to progress in core areas of MIR, but always with a clear focus on suitability for real-world applications. This paper is organized as follows. Each of the two projects is described in more detail in Sec. 2 and Sec. 3. Results from the research as well as the development perspective are reported. Finally, conclusions are given and future directions are sketched.

2

Songs2See

Musical education of children and adolescents is an important factor in their personal self-development regardless if it is about learning a musical instrument or in music courses at school. Children, adolescents and adults must be constantly motivated to practice and complete learning units. Traditional forms of teaching and even current e-learning systems are often unable to provide this motivation. On the other hand, music-based games are immensely popular [7], [17], but they fail to develop skills which are transferable to musical instruments [19]. Songs2See is set out to develop educational software for music learning which provides the motivation of game playing and at the same time develops real musical skills. Using music signal analysis as the key technology, we want to enable students to use popular musical instruments as game controllers for games which teach the students to play music of their own choice. This should be possible regardless of the representation of music they possess (audio, score, tab, chords, etc.). As a reward, the users receive immediate feedback from the automated analysis of their rendition. The game application will provide the students with visual and audio feedback regarding ﬁne-grained details of their performance with regard to timing (rhythm), intonation (pitch, vibrato), and articulation (dynamics). Central to the analysis is automatic music transcription, i.e., the extraction of a scalable symbolic representation from real-world music recordings using specialized computer algorithms [24], [11]. Such symbolic representation allows to render simultaneous visual and audible playbacks for the students, i.e., it can be translated to traditional notation, a piano-roll view or a dynamical animation showing the ﬁngering on the actual instrument. The biggest additional advantage is the possibility to let the students have their favorite song transcribed into a symbolic representation by the software. Thus, the students can play along to actual music they like, instead of speciﬁcally produced and edited learning pieces. In order to broaden the possibilities when creating 1 2

See for example http://last.fm See for example http://www.singstargame.com/

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exercises, state-of-the-art methods for audio source separation are exploited. Application of source separation techniques allows to attenuate accompanying instruments that obscure the instrument of interest or alternatively to cancel out the original instrument in order to create a play-along backing track [9]. It should be noted that we are not striving for hi-ﬁ audio quality with regards to the separation. It is more important, that the students can use the above described functionality to their advantage when practicing, a scenario in which a certain amount of audible artifacts is acceptable without being disturbing for the user. 2.1

Research Results

A glimpse of the main research directions shall be given here as an update to the overview given in [12]. Further details about the diﬀerent components of the system are to be published in [17]. Past research activities allowed us to use well-proven methods for timbre-based music segmentation [26], key detection [6] and beat grid extraction [40] out of the box. In addition, existing methods for automatic music transcription and audio source separation are advanced in the project. Automatic music transcription. We were able to integrate already available transcription algorithms that allow us to transcribe the drums, bass, main melody and chords in real-world music segments [11], [33]. In addition, we enable a manual error correction by the user that is helpful when dealing with diﬃcult signals (high degree of polyphony, overlapping percussive instruments etc.). With regard to the project goals and requirements of potential users it became clear that is is necessary to also transcribe monotimbral, polyphonic instruments like the piano and the guitar. Therefore, we conducted a review of the most promising methods for multi-pitch transcription. These comprise the iterative pitch-salience estimation in [23], the maximum likelihood approach in [13] and a combination of the Specmurt principle [34] with shift-invariant non-negative factorization [35]. Results show, that it is necessary to combine any of the aforementioned multi-pitch estimators with a chromagram-based [37] pre-processing to spare computation time. That is especially true for real-time polyphonic pitch detection. For the monophonic real-time case, we could successfully exploit the method pointed out in [18]. Audio source separation. We focused on assessing diﬀerent algorithms for audio source separation that have been reported in the literature. A thorough review, implementation and evaluation of the methods described in [14] was conducted. Inspection of the achievable results led to the conclusion, that sound separation based on tensor factorization is powerful but at the moment computationally too demanding to be applied in our project. Instead, we focused on investigating the more straightforward method described in [31]. This approach separates polyphonic music into percussive and harmonic parts via spectrogram diﬀusion. It has received much interest in the MIR community, presumably for

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its simplicity. An alternative approach for percussive vs. harmonic separation has been published in [10]. In that paper, further use of phase information in sound separation problems has been proposed. In all cases, phase information is complementary to the use of magnitude information. Phase contours for musical instruments exhibit similar micromodulations in frequency for certain instruments and can be an alternative of spectral instrument templates or instrument models. For the case of overlapped harmonics, phase coupling properties can be exploited. Although a multitude of further source separation algorithms has been proposed in the literature, only few of them make use of user interaction. In [36], a promising concept for using an approximate user input for extracting sound sources is described. Using a combination of the separation methods described in [9] and [20] in conjunction with user approved note transcriptions, we are able to separate melodic instruments from the background accompaniment in good quality. In addition, we exploit the principle described in [41] in order to allow the user to focus on certain instrument tracks that are well localized within the stereo panorama.

Fig. 1. Screenshot of Songs2See web application

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Development Results

In order to have “tangible” results early on, the software development started in parallel with the research tasks. Therefore, interfaces have been kept as generic as possible in order to enable adaption to alternative or extended core algorithms later on. Songs2See web application. In order to achieve easy accessibility to the exercise application, we decided to go for a web-based approach using Flex3 . Originally, the Flash application built with this tool did not allow direct processing of the microphone input. Thus, we had to implement a streaming server solution. We used the open source Red54 in conjunction with the transcoder library Xuggler5 . This way, we conducted real-time pitch detection [11] in the server application and returned the detected pitches to the web interface. Further details about the implementation are to be published in [18]. Only in their latest release in July 2010, i.e., Adobe Flash Player 10.1, has Flash incorporated the possibility of handling audio streams from a microphone input directly on the client side. A screenshot of the prototype interface is shown in Fig. 1. It can be seen that the user interface shows further assistance than just the plain score sheet. The ﬁngering on the respective instrument of the student is shown as an animation. The relative tones are displayed as well as the relative position of the pitch produced by the players’ instrument. This principle is well known from music games and has been adapted here for the educational purposes. Further helpful functions, such as transpose, tempo change and stylistic modiﬁcation will be implemented in the future. Songs2See editor. For the creation of music exercises, we developed an application with the working title Songs2See Editor. It is a stand-alone graphical user interface based on Qt6 that allows the average or expert user the creation of musical exercises. The editor already allows to go through the prototypical work-ﬂow. During import of a song, timbre segmentation is conducted and the beat grid and key candidates per segment are estimated. The user can choose the segment of interest, start the automatic transcription or use the source separation functionality. For immediate visual and audible feedback of the results, a piano-roll editor is combined with a simple synthesizer as well as sound separation controls. Thus, the user can grab any notes he suspects to be erroneously transcribed, move or delete them. In addition the user is able to seamlessly mix the ratio between the separated melody instrument and the background accompaniment. In Fig. 2, the interface can be seen. We expect that the users of the editor will creatively combine the diﬀerent processing methods in order to analyze and manipulate the audio tracks to their liking. In the current stage of development, export of MIDI and MusicXML is already possible. In a later stage support for other popular formats, such as TuxGuitar will be implemented. 3 4 5 6

See See See See

http://www.adobe.com/products/flex/ http://osflash.org/red5 http://www.xuggle.com/ http://qt.nokia.com/products

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Fig. 2. Screenshot of Songs2See editor prototype

3

GlobalMusic2One

GlobalMusic2one is developing a new generation of adaptive music search engines combining state-of-the-art methods of MIR with Web 2.0 technologies. It aims at reaching better quality in automated music recommendation and browsing inside global music collections. Recently, there has been a growing research interest in music outside the mainstream popular music from the so-called western culture group [39],[16]. For well-known mainstream music, large amounts of user generated browsing traces, reviews, play-lists and recommendations available in diﬀerent online communities can be analyzed through CF methods in order to reveal similarities between artists, songs and albums. For novel or niche content one obvious solution to derive such data is content-based similarity search. Since the early days of MIR, the search for music items related to a speciﬁc query song or a set of those (Query by Example) has been a consistent focus of scientiﬁc interest. Thus, a multitude of diﬀerent approaches with varying degree of complexity has been proposed [32]. Another challenge is the automatic annotation (a.k.a. “auto-tagging” [8]) of world music content. It is obvious that the broad term “World Music” is one of the most ill-deﬁned tags when being used to lump all “exotic genres” together. It lacks justiﬁcation because this category comprises such a huge variety of diﬀerent regional styles, inﬂuences, and a mutual mix up thereof. On the one hand, retaining the strict classiﬁcation paradigm for such a high variety of musical styles inevitably limits the precision and expressiveness of a classiﬁcation system that shall be applied to a world-wide genre taxonomy. With GlobalMusic2One, the user may create new categories allowing the system to ﬂexibly adapt to new musical forms of expression and regional contexts. These

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categories can, for example, be regional sub-genres which are deﬁned through exemplary songs or song snippets. This self-learning MIR framework will be continuously expanded with precise content-based descriptors. 3.1

Research Results

With automatic annotation of world music content, songs often cannot be assigned to one single genre label. Instead, various rhythmic, melodic and harmonic inﬂuences conﬂate into multi-layered mixtures. Common classiﬁer approaches fail due to their immanent assumption that for all song segments, one dominant genre exists and thus is retrievable. Multi-domain labeling. To overcome these problems, we introduced the “multi-domain labeling” approach [28] that breaks down multi-label annotations towards single-label annotations within diﬀerent musical domains, namely timbre, rhythm, and tonality. In addition, a separate annotation of each temporal segment of the overall song is enabled. This leads to a more meaningful and realistic two-dimensional description of multi-layered musical content. Related to that topic, classiﬁcation of singing vs. rapping in urban music has been described in [15]. In another paper [27] we applied the recently proposed Multiple Kernel Learning (MKL) technique that has been successfully used for real-world applications in the ﬁelds of computational biology, image information retrieval etc. In contrast to classic Support Vector Machines (SVM), MKL provides a possibility of weighting over diﬀerent kernels depending on a feature set. Clustering with constraints. Inspired by the work in [38], we investigated clustering with constraints with application to active exploration of music collections. Constrained clustering has been developed to improve clustering methods through pairwise constraints. Although these constraints are received as queries from a noiseless oracle, most of the methods involve a random procedure stage to decide which elements are presented to the oracle. In [29] we applied spectral clustering with constraints to a music dataset, where the queries for constraints were selected in a deterministic way through outlier identiﬁcation perspective. We simulated the constraints through the ground-truth music genre labels. The results showed that constrained clustering with the deterministic outlier identiﬁcation method achieved reasonable and stable results through the increment of the number of constraint queries. Although the constraints were enhancing the similarity relations between the items, the clustering was conducted in the static feature space. In [30] we embedded the information about the constraints to a feature selection procedure, that adapted the feature space regarding the constraints. We proposed two methods for the constrained feature selection: similarity-based and constrained-based. We applied the constrained clustering with embedded feature selection for the active exploration of music collections. Our experiments showed that the proposed feature selection methods improved the results of the constrained clustering.

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Rule-based classification. The second important research direction was rulebased classiﬁcation with high-level features. In general, high-level features can again be categorized according to diﬀerent musical domains like rhythm, harmony, melody or instrumentation. In contrast to low-level and mid-level audio features, they are designed with respect to music theory and are thus interpretable by human observers. Often, high-level features are derived from automatic music transcription or classiﬁcation into semantic categories. Diﬀerent approaches for the extraction of rhythm-related high-level features have been reported in [21], [25] and [1]. Although automatic extraction of high-level features is still quite error-prone, we proved in [4] that they can be used in a rule-based classiﬁcation scheme with a quality comparable to state-of-the-art pattern recognition using SVM. The concept of rule-based classiﬁcation was inspected in detail in [3] using a ﬁne granular manual annotation of high-level features referring to rhythm, instrumentation etc. In this paper, we tested rule-based classiﬁcation on a restricted dataset of 24 manually annotated audio tracks and achieved an accuracy rate of over 80%. Novel audio features. Adding the fourth domain instrumentation to the multi-domain approach described in Sec. 3.1 required the design and implementation of novel audio features tailored towards instrument recognition in polyphonic recordings. Promising results even with instruments from non-European cultural areas are reported in [22]. In addition, we investigated the automatic classiﬁcation of rhythmic patterns in global music styles in [42]. In this work, special measures have been taken to make the features and distance measures tempo independent. This is done implicitly, without the need for a preceding beat grid extraction that is commonly recommended in the literature to derive beat synchronous feature vectors. In conjunction with the approach to rule-based classiﬁcation described in Sec. 3.1, novel features for the classiﬁcation of bassplaying styles have been published in [5] and [2]. In this paper, we compared an approach based on high-level features and another one based on similarity measures between bass patterns. For both approaches, we assessed two diﬀerent strategies: classiﬁcation of patterns as a whole and classiﬁcation of all measures of a pattern with a subsequent accumulation of the classiﬁcation results. Furthermore, we investigated the inﬂuence of potential transcription errors on the classiﬁcation accuracy. Given a taxonomy consisting of 8 diﬀerent bass playing styles, best classiﬁcation accuracy values of 60.8% were achieved for the featurebased classiﬁcation and 68.5% for the pattern similarity approach. 3.2

Development Results

As with the Songs2See project, the development phase in GlobalMusic2One started in parallel with the research activities and is now near its completion. It was mandatory to have early prototypes of the required software for certain tasks in the project.

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Annotation Tool. We developed an Qt-based Annotation Tool that facilitates the gathering of conceptualized annotations for any kind of audio content. The program was designed for expert users to enable them to manually describe audio ﬁles eﬃciently on a very detailed level. The Annotation Tool can be conﬁgured to diﬀerent and extensible annotation schemes making it ﬂexible for multiple application ﬁelds. The tool supports single labeling, and multi-labeling, as well as the new approach of multi-domain labeling. However, strong labeling is not enforced by the tool, but remains under the control of the user. As a unique feature the audio annotation tool comes with a automated, timbre-based audio segmentation algorithm integrated that helps the user to intuitively navigate through the audio ﬁle during the annotation process and select the right segments. Of course, the granularity of the segmentation can be adjusted and every single segment border can be manually corrected if necessary. There are now approx. 100 observables that can be chosen from while annotating. This list is under steady development. In Fig. 3, a screenshot of the Annotation Tool conﬁgured to the Globalmusic2one description scheme can be seen.

Fig. 3. Screenshot of the Annotation Tool conﬁgured to the Globalmusic2one description scheme, showing the annotation of a stylistically and structurally complex song

PAMIR framework. The Personalized Adaptive MIR framework (PAMIR) is a server system written in Python that loosely strings together various functional blocks. These blocks are e.g., a relational database, a server for computing content-based similarities between music items and various machine learning servers. PAMIR is also the instance that enables the adaptivity with respect to the user preferences. Basically, it allows to conduct feature-selection and automated picking of the most suitable classiﬁcation strategy for a given classiﬁcation

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problem. Additionally, it enables content-based similarity search both on song and segment level. The latter method can be used to retrieve parts of songs, that are similar to the query, whereas the complete song may exhibit diﬀerent properties. Visualizations of the classiﬁcation and similarity-search results are delivered to the users via a web interface. In Fig. 4, the prototype web interface to the GlobalMusic2One portal is shown. It can be seen that we feature an intuitive similarity map for explorative browsing inside the catalog as well as target-oriented search masks for speciﬁc music properties. The same interface allows the instantiation of new concepts and manual annotation of reference songs. In the upper left corner, a segment player is shown that allows to jump directly to diﬀerent parts in the songs.

Fig. 4. Screenshot of the GlobalMusic2One prototype web client

4

Conclusions and Outlook

In this paper we presented an overview of the applied MIR projects Songs2See and GlobalMusic2One. Both address core challenges of MIR with strong focus to

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real-world applications. With Songs2See, development activities will be strongly directed towards implementation of more advanced features in the editor as well as the web application. The main eﬀorts inside GlobalMusic2One will be concentrated on consolidating the framework and the web client.

Acknowledgments The Thuringian Ministry of Economy, Employment and Technology supported this research by granting funds of the European Fund for Regional Development to the project Songs2See 7 , enabling transnational cooperation between Thuringian companies and their partners from other European regions. Additionally, this work has been partly supported by the German research project GlobalMusic2One 8 funded by the Federal Ministry of Education and Research (BMBF-FKZ: 01/S08039B).

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39. Tzanetakis, G., Kapur, A., Schloss, W.A., Wright, M.: Computational ethnomusicology. Journal of Interdisciplinary Music Studies 1(2), 1–24 (2007) 40. Uhle, C.: Automatisierte Extraktion rhythmischer Merkmale zur Anwendung in Music Information Retrieval-Systemen. Ph.D. thesis, Ilmenau University, Ilmenau, Germany (2008) 41. Vinyes, M., Bonada, J., Loscos, A.: Demixing commercial music productions via human-assisted time-frequency masking. In: Proceedings of the 120th AES convenction, Paris, France (2006), http://www.mtg.upf.edu/files/publications/ 271dd4-AES120-mvinyes-jbonada-aloscos.pdf (last viewed February 2011) 42. V¨ olkel, T., Abeßer, J., Dittmar, C., Großmann, H.: Automatic genre classiﬁcation of latin american music using characteristic rhythmic patterns. In: Proceedings of the Audio Mostly Conference (AMC), Pite˚ a, Sweden (2010)

MusicGalaxy: A Multi-focus Zoomable Interface for Multi-facet Exploration of Music Collections Sebastian Stober and Andreas N¨ urnberger Data & Knowledge Engineering Group Faculty of Computer Science Otto-von-Guericke-University Magdeburg, D-39106 Magdeburg, Germany {sebastian.stober,andreas.nuernberger}@ovgu.de http://www.dke-research.de

Abstract. A common way to support exploratory music retrieval scenarios is to give an overview using a neighborhood-preserving projection of the collection onto two dimensions. However, neighborhood cannot always be preserved in the projection because of the inherent dimensionality reduction. Furthermore, there is usually more than one way to look at a music collection and therefore diﬀerent projections might be required depending on the current task and the user’s interests. We describe an adaptive zoomable interface for exploration that addresses both problems: It makes use of a complex non-linear multi-focal zoom lens that exploits the distorted neighborhood relations introduced by the projection. We further introduce the concept of facet distances representing diﬀerent aspects of music similarity. User-speciﬁc weightings of these aspects allow an adaptation according to the user’s way of exploring the collection. Following a user-centered design approach with focus on usability, a prototype system has been created by iteratively alternating between development and evaluation phases. The results of an extensive user study including gaze analysis using an eye-tracker prove that the proposed interface is helpful while at the same time being easy and intuitive to use. Keywords: exploration, interface, multi-facet, multi-focus.

1

Introduction

There is a lot of ongoing research in the ﬁeld of music retrieval aiming to improve retrieval results for queries posed as text, sung, hummed or by example as well as to automatically tag and categorize songs. All these eﬀorts facilitate scenarios where the user is able to somehow formulate a query – either by describing the song or by giving examples. But what if the user cannot pose a query because the search goal is not clearly deﬁned? E.g., he might look for background music for a photo slide show but does not know where to start. All he knows is that he can tell if it is the right music the moment he hears it. In such a case, exploratory S. Ystad et al. (Eds.): CMMR 2010, LNCS 6684, pp. 273–302, 2011. c Springer-Verlag Berlin Heidelberg 2011

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similar

dissimilar Fig. 1. Possible problems caused by projecting objects represented in a highdimensional feature space (left) onto a low-dimensional space for display (right)

retrieval systems can help by providing an overview of the collection and letting the user decide which regions to explore further. When it comes to get an overview of a music collection, neighborhood-preserving projection techniques have become increasingly popular. Beforehand, the objects to be projected – depending on the approach, this may be artists, albums, tracks or any combination thereof – are analyzed to extract a set of descriptive features. (Alternatively, feature information may also be annotated manually or collected from external sources.) Based on these features, the objects can be compared – or more speciﬁcally: appropriate distance- or similarity measures can be deﬁned. The general objective of the projection can then be paraphrased as follows: Arrange the objects in two or three dimensions (on the display) in such a way that neighboring objects are very similar and the similarity decreases with increasing object distance (on the display). As the feature space of the objects to be projected usually has far more dimensions than the display space, the projection inevitably causes some loss of information – irrespective of which dimensionality reduction techniques is applied. Consequently, this leads to a distorted display of the neighborhoods such that some objects will appear closer than they actually are (type I error), and on the other hand some objects that are distant in the projection may in fact be neighbors in feature space (type II error). Such neighborhood distortions are depicted in Figure 1. These “projection errors” cannot be ﬁxed on a global scale without introducing new ones elsewhere as the projection is already optimal w.r.t. some criteria (depending on the technique used). In this sense, they should not be considered as errors made by the projection technique but of the resulting (displayed) arrangement. When a user explores a projected collection, type I errors increase the number of dissimilar (i.e. irrelevant) objects displayed in a region of interest. While this might become annoying, it is much less problematic than type II errors. They result in similar (i.e. relevant) objects to be displayed away from the region of interest – the neighborhood they actually belong to. In the worst case they could even be oﬀ-screen if the display is limited to the currently explored region. This way, a user could miss objects he is actually looking for.

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The interactive visualization technique described in this paper exploits these distorted neighborhood relations during user-interaction. Instead of trying to globally repair errors in the projection, the general idea is to temporarily ﬁx the neighborhood in focus. The approach is based on a multi-focus ﬁsh-eye lens that allows a user to enlarge and explore a region of interest while at the same time adaptively distorting the remaining collection to reveal distant regions with similar tracks. It can therefor be considered as a focus-adaptive distortion technique. Another problem that arises when working with similarity-based neighborhoods is that music similarity is highly subjective and may depend on a person’s background. Consequently, there is more than one way to look at a music collection – or more speciﬁcally to compare two tracks based on their features. The user-interface presented in this paper therefore allows the user to modify the underlying distance measure by adapting weights for diﬀerent aspects of (dis-)similarity. The remainder of this paper is structured as follows: Section 2 gives an overview of related approaches that aim to visualize a music collection. Subsequently, Section 3 outlines the approach developed in this work. The underlying techniques are addressed in Section 4 and Section 5 explains how a user can interact with the proposed visualization. In order to evaluate the approach, a user study has been conducted which is described in Section 6. Finally, Section 7 concludes with a brief summary.

2

Related Work

There exists a variety of approaches that in some way give an overview of a music collection. For the task of music discovery which is closely related to collection exploration, a very broad survey of approaches is given in [7]. Generally, there are several possible levels of granularity that can be supported, the most common being: track, album, artist and genre. Though a system may cover more than one granularity level (e.g. in [51] visualized as disc or TreeMap [41]), usually a single one is chosen. The user-interface presented in this paper focuses on the track level as do most of the related approaches. (However, like most of the other techniques, it may as well be applied on other levels such as for albums or artist. All that is required is an appropriate feature representation of the objects of interest.) Those approaches focusing on a single level can roughly be categorized into graph-based and similarity-based overviews. Graphs facilitate a natural navigation along relationship-edges. They are especially well-suited for the artist level as social relations can be directly visualized (as, e.g., in the Last.fm Artist Map 1 or the Relational Artist Map RAMA [39]). However, building a graph requires relations between the objects – either from domain knowledge or artiﬁcially introduced. E.g., there are some graphs that use similarity-relations obtained from external sources (such as the APIs of Last.fm 2 1 2

http://sixdegrees.hu/last.fm/interactive map.html http://www.last.fm/api

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or EchoNest 3 ) and not from an analysis of the objects themselves. Either way, this results in a very strong dependency and may quickly become problematic for less main-stream music where such information might not be available. This is why a similarity-based approach is chosen here instead. Similarity-based approaches require the objects to be represented by one or more features. They are in general better suited for track level overviews due the vast variety of content-based features that can be extracted from tracks. For albums and artists, either some means for aggregating the features of the individual tracks are needed or non-content-based features, e.g. extracted from knowledge resources like MusicBrainz 4 and Wikipedia 5 or cultural meta-data [54], have to be used. In most cases the overview is then generated using some metric deﬁned on these features which leads to proximity of similar objects in the feature space. This neighborhood should be preserved in the collection overview which usually has only two dimensions. Popular approaches for dimensionality reduction are Self-Organizing Maps (SOMs) [17], Principle Component Analysis (PCA) [14] and Multidimensional Scaling (MDS) techniques [18]. In the ﬁeld of music information retrieval, SOMs are widely used. SOM-based systems comprise the SOM-enhanced Jukebox (SOMeJB) [37], the Islands of Music [35,34] and nepTune [16], the MusicMiner [29], the PlaySOM - and PocketSOM -Player [30] (the latter being a special interface for mobile devices), the BeatlesExplorer [46] (the predecessor prototype of the system presented here), the SoniXplorer [23,24], the Globe of Music [20] and the tabletop applications MUSICtable [44], MarGrid [12], SongExplorer [15] and [6]. SOMs are prototypebased and thus there has to be a way to initially generate random prototypes and to modify them gradually when objects are assigned. This poses special requirements regarding the underlying feature space and distance metric. Moreover, the result depends on the random initialization and the neural network gradient descend algorithm may get stuck in a local minimum and thus not produce an optimal result. Further, there are several parameters that need to be tweaked according to the data set such as the learning rate, the termination criterion for iteration, the initial network structure, and (if applicable) the rules by which the structure should grow. However, there are also some advantages of SOMs: Growing versions of SOMs can adapt incrementally to changes in the data collection whereas other approaches may always need to generate a new overview from scratch. Section 4.2 will address this point more speciﬁcally for the approach taken here. For the interactive task at hand, which requires a realtime response, the disadvantages of SOMs outweigh their advantages. Therefore, the approach taken here is based on MDS. Given a set of data points, MDS ﬁnds an embedding in the target space that maintains their distances (or dissimilarities) as far as possible – without having to know their actual values. This way, it is also well suited to compute a layout for spring- or force-based approaches. PCA identiﬁes the axes of highest 3 4 5

http://developer.echonest.com http://musicbrainz.org http://www.wikipedia.org

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Fig. 2. In SoundBite [22], a seed song and its nearest neighbors are connected by lines

variance termed principal components for a set of data points in high-dimensional space. To obtain a dimensionality reduction to two-dimensional space, the data points are simply projected onto the two principal component axes with the highest variance. PCA and MDS are closely related [55]. In contrast to SOMs, both are non-parametric approaches that compute an optimal solution (with respect to data variance maximization and distance preservation respectively) in ﬁxed polynomial time. Systems that apply PCA, MDS or similar force-based approaches comprise [4], [11], [10], the fm4 Soundpark [8], MusicBox [21], and SoundBite [22]. All of the above approaches use some kind of projection technique to visualize the collection but only a small number tries to additionally visualize properties of the projection itself: The MusicMiner [29] draws mountain ranges between songs that are displayed close to each other but dissimilar. The SoniXplorer [23,24] uses the same geographical metaphor but in a 3D virtual environment that the user can navigate with a game pad. The Islands of Music [35,34] and its related approaches [16,30,8] use the third dimension the other way around: Here, islands or mountains refer to regions of similar songs (with high density). Both ways, local properties of the projection are visualized – neighborhoods of either dissimilar or similar songs. In contrast (and possibly as a supplementation) to this, the technique proposed in this paper aims to visualize properties of the projection that are not locally conﬁned: As visualized in [32], there may be distant regions in a projection that contain very similar objects. This is much like a “wormhole” connecting both regions through the high-dimensional feature space. To our knowledge, the only attempt so far to visualize such distortions caused by the projection is described in [22]. The approach is to draw lines that connect a selected seed track (highlighted with a circle) with its neighbors as shown in Figure 2.

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Additionally, our goal is to support user-adaptation during the exploration process by means of weighting aspects of music similarity. Of the above approaches, only the revised SoniXplorer [24], MusicBox [21] and the BeatlesExplorer [46] – our original SOM-based prototype – allow automatic adaptation of the view on the collection through interaction. Apart from this, there exist systems that also adapt a similarity measure but not to change the way the collection is presented in an overview but to directly generate playlists: MPeer [3] allows to navigate the similarity space deﬁned by the audio content, the lyrics and cultural meta-data collected from the web through an intuitive joystick interface. In the E-Mu Jukebox [53], weights for ﬁve similarity components (sound, tempo, mood, genre and year – visually represented by adapters) can be changed by dragging them on a bull’s eye. PATS (Personalized Automatic Track Selection) [36] and the system described in [56] do not require manual adjustment of the underlying similarity measure but learn from the user as he selects songs that in his opinion do not ﬁt to the current context-of-use. PAPA (Physiology and Purpose-Aware Automatic Playlist Generation) [33] as well as the already commercially available BODiBEAT music player6 uses sensors that measure several bio-signals (such as the pulse) of the user as immediate feedback for the music currently played. In this case not even a direct interaction of the user with the system is required to continuously adapt playlist-models for diﬀerent purposes. However, as we have shown in a recent survey [50], users do not particularly like the idea of having their bio-signals logged – especially if they cannot control the impact of this information on the song recommendation process. In contrast to these systems that purely focus on the task of playlist generation, we pursuit a more general goal in providing an adaptive overview of the collection that can then be used to easily generate playlists as e.g. already shown in [16] or [21].

3

Outline

The goal of our work is to provide a user with an interactive way of exploring a music collection that takes into account the above described inevitable limitations of a low-dimensional projection of a collection. Further, it should be applicable for realistic music collections containing several thousands of tracks. The approach taken can be outlined as follows: – An overview of the collection is given, where all tracks are displayed as points at any time. For a limited number of tracks that are chosen to be spatially well distributed and representative, an album cover thumbnail is shown for orientation. – The view on the collection is generated by a neighborhood-preserving projection (e.g. MDS, SOM, PCA) from some high-dimensional feature space onto two dimensions. I.e., in general tracks that are close in feature space will likely appear as neighbors in the projection. 6

http://www.yamaha.com/bodibeat

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– Users can adapt the projection by choosing weights for several aspects of music (dis-)similarity. This gives them the possibility to look at a collection from diﬀerent perspectives. (This adaptation is purely manual, i.e. the visualization as described in this paper is only adaptable w.r.t. music similarity. Techniques to further enable adaptive music similarity are, e.g., discussed in [46,49].) – In order to allow immediate visual feedback in case of similarity adaptation, the projection technique needs to guarantee near real-time performance – even for large music collections. The quality of the produced projection is only secondary – a perfect projection that correctly preserves all distances between all tracks is extremely unlikely anyways. – The projection will inevitably contain distortions of the actual distances of the tracks. Instead of trying to improve the quality of the projection method and trying to ﬁx heavily distorted distances, they are exploited during interaction with the projection: The user can zoom into a region of interest. The space for this region is increased, thus allowing to display more details. At the same time the surrounding space is compacted but not hidden from view. This way, there remains some context for orientation. To accomplish such a behavior the zoom is based on a non-linear distortion similar to so called “ﬁsh-eye” lenses. At this point the original (type II) projection errors come into play: Instead of putting a single lens focus on the region of interest, additional focuses are introduced in regions that contain tracks similar to those in primary focus. The resulting distortion brings original neighbors back closer to each other. This gives the user another option for interactive exploration. Figure 3 depicts the outline of the approach. The following sections cover the underlying techniques (Section 4) and the user-interaction (Section 5) in detail.

4

Underlying Techniques

4.1

Features and Facets

The prototype system described here uses collections of music tracks. As a prerequisite, it is assumed that the tracks are represented by some descriptive features that can, e.g., be extracted, manually annotated or obtained form external sources. In the current implementation, content-based features are extracted utilizing the capabilities of the frameworks CoMIRVA [40] and JAudio [28]. Speciﬁcally, Gaussian Mixture Models of the Mel Frequency Cepstral Coeﬃcients (MFCCs) according to [2] and [26] and “ﬂuctuation patterns” describing how strong and fast beats are played within speciﬁc frequency bands [35] are computed with CoMIRVA. JAudio is used to extract a global audio descriptor “MARSYAS07” as described in [52]. Further, lyrics for all songs were obtained through the web service of LyricWiki7 , ﬁltered for stop words, stemmed and described by document vectors with TFxIDF term weights [38]. Additional features 7

http://lyricwiki.org

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Fig. 3. Outline of the approach showing the important processing steps and data structures. Top: preprocessing. Bottom: interaction with the user with screenshots of the graphical user interface.

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that are currently only used for the visualization are ID3 tags (artist, album, title, track number and year) extracted from the audio ﬁles, track play counts obtained from a Last.fm proﬁle, and album covers gathered through web search. Distance Facets. Based on the features associated with the tracks, facets are deﬁned (on subspaces of the feature space) that refer to diﬀerent aspects of music (dis-)similarity. This is depicted in Figure 3 (top). Definition 1. Given a set of features F , let S be the space determined by the feature values for a set of tracks T . A facet f is defined by a facet distance measure δf on a subspace Sf ⊆ S of the feature space, where δf satisfies the following conditions for any x, y ∈ T : – δ(x, y) ≥ 0 and δ(x, y) = 0 if and only if x = y – δ(x, y) = δ(y, x) (symmetry) Optionally, δ is a distance metric if it additionally obeys the triangle inequality for any x, y, z ∈ T : – δ(x, z) ≤ δ(x, y) + δ(y, z) (triangle inequality) E.g., a facet “timbre” could be deﬁned on the MFCC-based feature described in [26] whereas a facet “text” could compare the combined information from the features “title” and “lyrics”. It is important to stress the diﬀerence to common faceted browsing and search approaches that rely on a faceted classiﬁcation of objects to support users in exploration by ﬁltering available information. Here, no such ﬁltering by value is applied. Instead, we employ the concept of facet distances to express diﬀerent aspects of (dis-)similarity that can be used for ﬁltering. Facet Distance Normalization. In order to avoid a bias when aggregating several facet distance measures, the values should be normalized. The following normalization truncates very high facet distance values δf (x, y) of a facet f and results in a value range of [0, 1] δf (a, b) δf (a, b) = min 1, (1) μ+σ where μ is the mean μ=

1 |{(x, y) ∈ T 2 }|

and σ is the standard deviation 1 σ= |{(x, y) ∈ T 2 }|

(x,y)∈T 2

of all distance values with respect to δf .

δf (x, y)

(2)

(x,y)∈T 2

(δf (x, y) − μ)2

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S. Stober and A. N¨ urnberger Table 1. Facets deﬁned for the current implementation facet name

feature

distance metric

timbre rhythm dynamics lyrics

GMM of MFCCs ﬂuctuation patterns MARSYAS07 TFxIDF weighted term vectors

Kullback-Leibler divergence euclidean distance euclidean distance cosine distance

Facet Distance Aggregation. The actual distance between tracks x, y ∈ T w.r.t. to the facets f1 , . . . , fl can be computed by aggregating the individual facet distances δf1 (x, y), . . . , δfl (x, y). For the aggregation, basically any function could be used. Common parametrized aggregation functions are: l 2 – d= i=1 wi δfi (x, y) (weighted euclidean distance) l – d = i=1 wi δfi (x, y)2 (squared weighted eucl. distance) l – d = i=1 wi δfi (x, y) (weighted sum) – d = maxi=1..l {wi δfi (x, y)} (maximum) – d = mini=1..l {wi δfi (x, y)} (minimum) These aggregation functions allow to control the importance of the facet distances d1 , . . . , dl through their associated weights w1 , . . . , wl . Default settings for the facet weights and the aggregation function are deﬁned by an expert (who also deﬁned the facets themselves) and can later be adapted by the user during interaction with the interface. Table 1 lists the facets used in the current implementation. 4.2

Projection

In the projection step shown in Figure 3 (bottom), the position of all tracks on the display is computed according to their (aggregated) distances in the highdimensional feature space. Naturally, this projection should be neighborhoodpreserving such that tracks close to each other in feature space are also close in the projection. We propose to use a landmark- or pivot-based Multidimensional Scaling approach (LMDS) for the projection as described in detail in [42,43]. This is a computationally eﬃcient approximation to classical MDS. The general idea of this approach is as follows: A representative sample of objects – called “landmarks” – is drawn randomly from the whole collection.8 For this landmark sample, an embedding into low-dimensional space is computed using classical MDS. The remaining objects can then be located within this space according to their distances to the landmarks. 8

Alternatively, the MaxMin heuristic (greedily seeking out extreme, well-separated landmarks) could be used – with the optional modiﬁcation to replace landmarks with a predeﬁned probability by randomly chosen objects (similar to a mutation operator in genetic programming). Neither alternative seems to produce less distorted projections while having much higher computational complexity. However, there is possibly some room for improvement here but this is out of the scope of this paper.

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Complexity. Classical MDS has a computational complexity of O(N 3 ) for the projection, where N is the number of objects in the data set. Additionally, the N ×N distance matrix needed as input requires O(N 2 ) space and is computed in O(CN 2 ), where C are the costs of computing the distance between two objects. By limiting the number of landmark objects m N , an LMDS projection can be computed in O(m3 + kmN ), where k is the dimension of the visualization space, which is ﬁxed here as 2. The ﬁrst part refers to the computation of the classical MDS for the m landmarks and the second to the projection of the remaining objects with respect to the landmarks. Further, LMDS requires only the distances of each data point to the landmarks, i.e. only a m × N distance matrix has to be computed resulting in O(mN ) space and O(CmN ) computational complexity. This way, LMDS becomes feasible for application on large data sets as it scales linearly with the size of the data set. Facet Distance Caching. The computation of the distance matrix that is required for LMDS can be very time consuming – not only depending on the size of the collection and landmark sample but also on the number of facets and the complexity of the respective facet distance measures. Caching can reduce the amount of information that has to be recomputed. Assuming a ﬁxed collection, the distance matrix only needs to be recomputed if the facet weights or the facet aggregation function change. Moreover, even a change of the aggregation parameters has no impact on the facet distances. This allows to pre-compute for each track the distance values to all landmarks for all facets oﬄine and store them in the 3-dimensional data structure depicted in Figure 3 (top) called “facet distance cuboid”. It is necessary to store the facet distance values separately as it is not clear at indexing time how these values are to be aggregated. During interaction with the user, when near real-time response is required, only the computational lightweight facet distance aggregation that produces the distance matrix from the cuboid and the actual projection need to be done. If N is the number of tracks, m the number of landmarks and l the number of facets, the cuboid has the dimension N × m × l and holds as many distance values. Note that m and l are ﬁxed small values of O(100) and O(10) respectively. Thus, the space requirement eﬀectively scales linearly with N and even for large N the data structure should ﬁt into memory. To further reduce the memory requirements of this data structure, the distance values are discretized to the byte range ([0 . . . 255]) after normalization to [0, 1] as described in Section 4.1. Incremental Collection Updates. Re-computation becomes also necessary once the collection changes. In previous work [32,46] we used a Growing SelfOrganizing Map approach (GSOM) for the projection. While both approaches, LMDS and GSOM, are neighborhood-preserving, GSOMs have the advantage of being inherently incremental, i.e. adding or removing objects from the data set only gradually changes the way, the data is projected. This is a nice characteristic because too abrupt changes in the projection caused by adding or removing some tracks might irritate the user if he has gotten used to a speciﬁc projection. On the contrary, LMDS does not allow for incremental changes of the projection.

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Fig. 4. The SpringLens particle mesh is distorted by changing the rest-length of selected springs

However, it still allows objects to be added or removed from the data set to some extend without the need to compute a new projection: If a new track is added to the collection, an additional “layer” has to be appended to the facet distance cuboid containing the facet distances of the new track with all landmarks. The new track can then be projected according to these distances. If a track is removed, the respective “layer” of the cuboid can be deleted. Neither operation does further alter the projection.9 Adding or removing many tracks may however alter the distribution of the data (and thus the covariances) in such a way that the landmark sample may no longer be representative. In this case, a new projection based on a modiﬁed landmark sample should be computed. However, for the scope of this paper, a stable landmark set is assumed and this point is left for further work. 4.3

Lens Distortion

Once the 2-D-positions of all tracks are computed by the projection technique, the collection could already be displayed. However, an intermediate distortion step is introduced as depicted in Figure 3 (bottom). It serves as the basis for the interaction techniques described later. Lens Modeling. The distortion technique is based on an approach originally developed to model complex nonlinear distortions of images called “SpringLens” [9]. A SpringLens consists of a mesh of mass particles and interconnecting springs that form a rectangular grid with ﬁxed resolution. Through the springs, forces are exerted between neighboring particles aﬀecting their motion. By changing the rest-length of selected springs, the mesh can be distorted as depicted in Figure 4. (Further, Figure 3 (bottom) and Figure 7 shows larger meshes simulating lenses.) The deformation is calculated by a simple iterative physical simulation over time using an Euler integration [9]. In the context of this work, the SpringLens technique is applied to simulate a complex superimposition of multiple ﬁsh-eye lenses. A moderate resolution is 9

In case a landmark track is removed from the collection, its feature representation has to be kept to be able to compute facet distances for new tracks. However, the corresponding “layer” in the cuboid can be removed as for any ordinary track.

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chosen with a maximum of 50 cells in each dimension for the overlay mesh which yields suﬃcient distortion accuracy while real-time capability is maintained. The distorted position of the projection points is obtained by barycentric coordinate transformation with respect to the particle points of the mesh. Additionally, z-values are derived from the rest-lengths that are used in the visualization to decide whether an object has to be drawn below or above another one. Nearest Neighbor Indexing. For the adaptation of the lens distortion, the nearest neighbors of a track need to be retrieved. Here, the two major challenges are: 1. The facet weights are not known at indexing time and thus the index can only be built using the facet distances. 2. The choice of an appropriate indexing method for each facet depends on the respective distance measure and the nature of the underlying features. As the focus lies here on the visualization and not the indexing, only a very basic approach is taken and further developments are left for future work: A limited list of nearest neighbors in pre-computed for each track. This way, nearest neighbors can be retrieved by simple lookup in constant time (O(1)). However, updating the lists after a change of the facet weights is computationally expensive. While the resulting delay of the display update is still acceptable for collections with a few thousands tracks, it becomes infeasible for larger N . For more eﬃcient index structures, it may be possible to apply generic multimedia indexing techniques such as space partition trees [5] or approximate approaches based on locality sensitive hashing [13] that may even be kernelized [19] to allow for more complex distance metrics. Another option is to generate multiple nearest neighbor indexes – each for a diﬀerent setting of the facet weights – and interpolate the retrieved result lists w.r.t. to the actual facet weights. 4.4

Visualization Metaphor

The music collection is visualized as a galaxy. Each track is displayed as a star or as its album cover. The brightness and (to some extend) the hue of stars depends on a predeﬁned importance measure. The currently used measure of importance is the track play count obtained from the Last.fm API and normalized to [0, 1] (by dividing by the maximum value). However, this could also be substituted by a more sophisticated measure, e.g. based on (user) ratings, chart positions or general popularity. The size and the z-order (i.e. the order of objects along the z-axis) of the objects depends on their distortion z-values. Optionally, the SpringLens mesh overlay can be displayed. The visualization then resembles the space-time distortions well known from gravitational and relativistic physics. 4.5

Filtering

In order to reduce the amount of information displayed at a time, an additional ﬁltering step is introduced as depicted in Figure 3 (bottom). The user

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Fig. 5. Available ﬁlter modes: collapse all (top left), focus (top right), sparse (bottom left), expand all (bottom right). The SpringLens mesh overlay is hidden.

can choose between diﬀerent ﬁlters that decide whether a track is displayed collapsed or expanded – i.e. as a star or album cover respectively. While album covers help for orientation, the displayed stars give information about the data distribution. Trivial ﬁlters are those displaying no album covers (collapseAll) or all (expandAll). Apart from collapsing or expanding all tracks, it is possible to expand only those tracks in magniﬁed regions (i.e. with a z-level above a predeﬁned threshold) or to apply a sparser filter. The results of using these ﬁlter modes are shown in Figure 5. A sparser ﬁlter selects only a subset of the collection to be expanded that is both, sparse (well distributed) and representative. Representative tracks are those with a high importance (described in Section 4.4). The ﬁrst sparser version used a Delaunay triangulation and was later substituted by a raster-based approach that produces more appealing results in terms of the spatial distribution of displayed covers.

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Originally, the set of expanded tracks was updated after any position changes caused by the distortion overlay. However, this was considered irritating during early user tests and the sparser strategy was changed to update only if the projection or the displayed region changes. Delaunay Sparser Filter. This sparser ﬁlter constructs a Delaunay triangulation incrementally top-down starting with the track with the highest importance and some virtual points at the corners of the display area. Next, the size of all resulting triangles given by the radius of their circumcircle is compared with a predeﬁned threshold sizemin . If the size of a triangle exceeds this threshold, the most important track within this triangle is chosen for display and added as a point for the triangulation. This process continues recursively until no triangle that exceeds sizemin contains anymore tracks that could be added. All tracks belonging to the triangulation are then expanded (i.e. displayed as album thumbnail). The Delaunay triangulation can be computed in O(n log n) and the number of triangles is at most O(n) with n N being the number of actually displayed album cover thumbnails. To reduce lookup time, projected points are stored in a quadtree data structure [5] and sorted by importance within the tree’s quadrants. A triangle’s size may change through distortion caused by the multifocal zoom. This change may trigger an expansion of the triangle or a removal of the point that caused its creation originally. Both operations are propagated recursively until all triangles meet the size condition again. Figure 3 (bottom) shows a triangulation and the resulting display for a (distorted) projection of a collection. Raster Sparser Filter. The raster sparser ﬁlter divides the display into a grid of quadratic cells. The size of the cells depends on the screen resolution and the minimal display size of the album covers. Further, it maintains a list of the tracks ranked by importance that is precomputed and only needs to be updated when the importance values change. On an update, the sparser runs through its ranked list. For each track it determines the respective grid cell. If the cell and the surrounding cells are empty, the track is expanded and its cell blocked. (Checking surrounding cells avoids image overlap. The necessary radius for the surrounding can be derived from the cell and cover sizes.) The computational complexity of this sparser approach is linear in the number of objects to be considered but also depends on the radius of the surrounding that needs to be checked. The latter can be reduced by using a data structure for the raster that has O(1) look-up complexity but higher costs for insertions which happen far less frequently. This approach has further the nice property that it handles the most important objects ﬁrst and thus even if interrupted returns a useful result.

5

Interaction

While the previous section covered the underlying techniques, this section describes how users can interact with the user-interface that is built on top of

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them. Figure 6 shows a screenshot of the MusicGalaxy prototype.10 It allows several ways of interacting with the visualization: Users can explore the collection through common panning & zooming (Section 5.1). Alternatively, they can use the adaptive multi-focus technique introduced with this prototype (Section 5.2). Further, they can change the facet aggregation function parameters and this way adapt the view on the collection according to their preferences (Section 5.3). Hovering over a track displays its title and a double-click start the playback that can be controlled by the player widget at the bottom of the interface. Apart from this, several display parameters can be changed such as the ﬁltering mode (Section 4.5), the size of the displayed album covers or the visibility of the SpringLens overlay mesh. 5.1

Panning and Zooming

These are very common interaction techniques that can e.g. be found in programs for geo-data visualization or image editing that make use of the map metaphor. Panning shifts the displayed region whereas zooming decreases or increases it. (This does not aﬀect the size of the thumbnails which can be controlled separately using the PageUp and PageDn keys.) Using the keyboard, the user can pan with the cursor keys and zoom in and out with + and – respectively. Alternatively, the mouse can be used: Clicking and holding the left button while moving the mouse pans the display. The mouse wheel controls the zoom level. If not the whole collection can be displayed, an overview window indicating the current section is shown in the top left corner, otherwise it is hidden. Clicking into the overview window centers the display around the respective point. Further, the user can drag the section indicator around which also results in panning. 5.2

Focusing

This interaction techniques allows to visualize – and to some extend alleviate – the neighborhood distortions introduced by the dimensionality reduction during the projection. The approach is based on a multi-focus ﬁsh-eye lens that is implemented using the SpringLens distortion technique (Section 4.3). It consists of a user-controlled primary focus and a neighborhood-driven secondary focus. The primary focus is a common ﬁsh-eye lens. By moving this lens around (holding the right mouse button), the user can zoom into regions of interest. In contrast to the basic linear zooming function described in Section Section 5.1, this leads to a nonlinear distortion of the projection. As a result, the region of interest is enlarged making more space to display details. At the same time, less interesting regions are compacted. This way, the user can closely inspect the region of interest without loosing the overview as the ﬁeld of view is not narrowed (as opposed to the linear zoom). The magniﬁcation factor of the lens can be changed using the mouse wheel while holding the right mouse button. The 10

A demo video is available at: http://www.dke-research.de/aucoma

Fig. 6. Screenshot of the MusicGalaxy prototype with visible overview window (top left), player (bottom) and SpringLens mesh overlay (blue). In this example, a strong album eﬀect can be observed as for the track in primary focus, four tracks of the same album are nearest neighbors in secondary focus.

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Fig. 7. SpringLens distortion with only primary focus (left) and additional secondary focus (right)

visual eﬀect produced by the primary zoom resembles a 2-dimensional version of the popular “cover ﬂow” eﬀect. The secondary focus consist of multiple such ﬁsh-eye lenses. These lenses are smaller and cannot be controlled by the user but are automatically adapted depending on the primary focus. When the primary focus changes, the neighbor index (Section 4.3) is queried with the track closest to the center of focus. If nearest neighbors are returned that are not in the primary focus, secondary lenses are added at the respective positions. As a result, the overall distortion of the projection brings the distant nearest neighbors back closer to the focused region of interest. Figure 7 shows the primary and secondary focus with visible SpringLens mesh overlay. As it can become tiring to hold the right mouse button while moving the focus around, the latest prototype introduces a focus lock mode (toggled with the return key). In this mode, the user clicks once to start a focus change and a second time to freeze the focus. To indicate that the focus is currently being changed (i.e. mouse movement will aﬀect the focus), an icon showing a magnifying glass is displayed in the lower left corner. The secondary focus is by default always updated instantly when the primary focus changes. This behavior can be disabled resulting only in an update of the secondary focus once the primary focus does not change anymore. 5.3

Adapting the Aggregation Functions

Two facet control panels allow to adapt two facet distance aggregation functions by choosing one of the function types listed in Section 4.1 (from a drop-down menu) and adjusting weights for the individual facets (through sliders). The control panels are hidden in the screenshot Figure 6) but shown in Figure 3 (bottom) that depicts the user-interaction. The ﬁrst facet distance aggregation function

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is applied to derive the track-landmark distances from the facet distance cuboid (Section 4.2). These distances are then used to compute the projection of the collection. The second facet distance aggregation function is applied to identify the nearest neighbors of a track and thus indirectly controls the secondary focus. Changing the aggregation parameters results in a near real-time update of the display so that the impact of the change becomes immediately visible: In case of the parameters for the nearest neighbor search, some secondary focus region may disappear while somewhere else a new one appears with tracks now considered more similar. Here, the transitions are visualized smoothly due to the underlying physical simulation of the SpringLens grid. In contrast to this, a change of the projection similarity parameters has a more drastic impact on the visualization possibly resulting in a complete re-arrangement of all tracks. This is because the LMDS projection technique produces solutions that are unique only up to translation, rotation, and reﬂection and thus, even a small parameter change may, e.g., ﬂip the visualization. As this may confuse users, one direction of future research is to investigate how the position of the landmarks can be constrained during the projection to produce more gradual changes. The two facet distance aggregation functions are linked by default as it is most natural to use the same distance measure for projection and neighbor retrieval. However, unlinking them and using e.g. orthogonal distance measures can lead to interesting eﬀects: For instance, one may choose to compute the collection based solely on acoustic facets and ﬁnd nearest neighbors for the secondary focus through lyrics similarity. Such a setting would help to uncover tracks with a similar topic that (most likely) sound very diﬀerent.

6

Evaluation

The development of MusicGalaxy followed a user-driven design approach [31] by iteratively alternating between development and evaluation phases. The ﬁrst prototype [47] was presented at the CeBIT 2010 11 a German trade fair specialized on information technology, in early March 2010. During the fair, feedback was collected from a total of 112 visitors aged between 16 and 63 years. The general reception was very positive. The projection-based visualization was generally welcomed as an alternative to common list views. However, some remarked that additional semantics of the two display axis would greatly improve orientation. Young visitors particularly liked the interactivity of the visualization whereas older ones tended to have problems with this. They stated that the reason lay in the amount of information displayed which could still be overwhelming. To address the problem, they proposed to expand only tracks in focus, increase the size of objects in focus (compared to the others) and hide the mesh overlay as the focus would be already visualized by the expanded and enlarged objects. All of these proposals have been integrated into the second prototype. The second prototype was tested thoroughly by three testers. During these tests, the eye movements of the users were recorded with an Tobii T60 11

http://www.cebit.de

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eye-tracker that can capture where and how long the gaze of the participants rests for some time (referred to as “ﬁxation points”). Using the adaptive SpringLens focus, the mouse generally followed the gaze that scans the border of the focus in order to decide on the direction to explore further. This resulted in a much smoother gaze-trajectory than the one observed during usage of panning and zooming where the gaze frequently switched between the overview window and the objects of interest – as not to loose orientation. This indicates that the proposed approach is less tiring for the eyes. However, the testers criticized the controls used to change the focus – especially having to hold the right mouse button all the time. This lead to the introduction of the focus lock mode and several minor interface improvements in the third version of the prototype [48] that are not explicitly covered here. The remainder of this section describes the evaluation of the third MusicGalaxy prototype in a user study [45] with the aim to proof that the userinterface indeed helps during exploration. Screencasts of 30 participants solving an exploratory retrieval task were recorded together with eye-tracking data (again using a Tobii T60 eye-tracker) and web cam video streams. This data was used to identify emerging search strategies among all users and to analyze to what extent the primary and secondary focus was used. Moreover, ﬁrst-hand impressions of the usability of the interface were gathered by letting the participants say aloud whatever they think, feel or remark as they go about their task (think-aloud protocol). In order to ease the evaluation, the study was not conducted with the original MusicGalaxy user-interface prototype but with a modiﬁed version that can handle photo collections depicted in Figure 8. It relies on widely used MPEG-7 visual descriptors (EdgeHistogram, ScalableColor and ColorLayout ) [27,25] to compute the visual similarity (see [49] for further details) – replacing the originally used music features and respective similarity facets. Using photo collections for evaluation instead of music has several advantages: It can be assured that none of the participants knows any of the photos in advance what could otherwise introduce some bias. Dealing with music, this would be much harder to realize. Furthermore, similarity and relevance of photos can be assessed in an instant. This is much harder for music tracks and requires additional time for listening – especially if the tracks are previously unknown. The following four questions were addressed in the user study: 1. How does the lens-based user-interface compare in terms of usability to common panning & zooming techniques that are very popular in interfaces using a map metaphor (such as Google Maps 12 )? 2. How much do users actually use the secondary focus or would a common ﬁsh-eye distortion (i.e. only the primary focus) be suﬃcient? 3. What interaction patterns do emerge? 4. What can be improved to further support the user and increase user satisfaction? 12

http://maps.google.com

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Fig. 8. PhotoGalaxy – a modiﬁed version of MusicGalaxy for browsing photo collections that was used during the evaluation (color scheme inverted)

To answer the ﬁrst question, participants compared a purely SpringLens-based user-interface with a common panning & zooming and additionally a combination of both. For questions 2 and 3, the recorded interaction of the participants with the system was analyzed in detail. Answers to question 4 were collected by asking the users directly for missing functionality. Section 6.1 addresses the experimental setup in detail and Section 6.2 discusses the results. 6.1

Experimental Setup

At the beginning of the experiment, the participants were asked several questions to gather general information about their background. Afterwards, they were presented four image collections (described below) in ﬁxed order. On the ﬁrst collection, a survey supervisor gave a guided introduction to the interface and the possible user actions. Each participant could spent as much time as needed to get used to the interface. Once, the participant was familiar with the controls, she or he continued with the other collections for which a retrieval task (described below) had to be solved without the help of the supervisor. At this point, the participants were divided into two groups. The ﬁrst group used only panning & zooming (P&Z) as described in Section 5.1 on the second collection and only the SpringLens functionality (SL) described in Section 5.2 on the third one. The other group started with SL and then used P&Z. The order of the datasets stayed the same for both groups. (This way, eﬀects caused by the order of the approaches and slightly varying diﬃculties among the collections are avoided.) The fourth collection could then be explored by using both, P&Z and SL. (The functionality

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Collection

Topics (number of images)

Melbourne & Victoria – Barcelona Tibidabo (12), Sagrada Fam´ılia (31), Stone Hallway in Park G¨ uell (13), Beach & Sea (29), Casa Mil` a (16) Japan Owls (10), Torii (8), Paintings (8), Osaka Aquarium (19), Traditional Clothing (35) Western Australia Lizards (17), Aboriginal Art (9), Plants (Macro) (17), Birds (21), Ningaloo Reef (19)

for adapting the facet distance aggregation functions described in Section 5.3 was deactivated for the whole experiment.) After the completion of the last task, the participants were asked to assess the usability of the diﬀerent approaches. Furthermore, feedback was collected pointing out, e.g., missing functionality. Test Collections. Four image collection were used during the study. They were drawn from a personal photo collection of the authors.13 Each collection comprises 350 images – except the ﬁrst collection (used for the introduction of the user-interface) which only contains 250 images. All images were scaled down to ﬁt 600x600 pixels. For each of the collections 2 to 4, ﬁve non-overlapping topics were chosen and the images annotated accordingly. These annotation served as ground truth and were not shown to the participants. Table 2 shows the topics for each collection. In total, 264 of the 1050 images belong to one of the 15 topics. Retrieval Task. For the collections 2 to 4, the participants had to ﬁnd ﬁve (or more) representative images for each of the topics listed in Table 2. As guidance, handouts were prepared that showed the topics – each one printed in a diﬀerent color –, an optional brief description and two or three sample images giving an impression what to look for. Images representing a topic had to be marked with the topic’s color. This was done by double clicking on the thumbnail what opened a ﬂoating dialog window presenting the image at big scale and allowing the participant to classify the image to a predeﬁned topic by clicking a corresponding button. As a result, the image was marked with the color representing the topic. Further, the complete collection could be ﬁltered by highlighting all thumbnails classiﬁed to one topic. This was done by pressing the numeric key (1 to 5) for the respective topic number. Highlighting was done by focusing a ﬁsh-eye lens on every marked topic member and thus enlarging the corresponding thumbnails. It was pointed out that the decision whether an image was representative for a group was solely up to the participant and not judged otherwise. There was no time limit for the task. However, the participants were encouraged to skip to

13

The collections and topic annotations are publicly available under the Creative Commons Attribution-Noncommercial-Share Alike license, http://creativecommons.org/licenses/by-nc-sa/3.0/ Please contact [email protected].

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the next collection after approximately ﬁve minutes as during this time already enough information would have been collected. Tweaking the Nearest Neighbor Index. In the original implementation, at most ﬁve nearest neighbors are retrieved with the additional constraint that their distance to the query object has to be in the 1-percentile of all distances in the collection. (This avoids returning nearest neighbors that are not really close.) 264 of the 1050 images belonging to collections 2 to 4 have a ground truth topic label. For only 61 of these images, one or more of the ﬁve nearest neighbors belonged to the same topic and only in these cases, the secondary focus would have displayed something helpful for the given retrieval task. This let us conclude that the feature descriptors used were not sophisticated enough to capture the visual intra-topic similarity. A lot more work would have been involved to improve the features – but this would have been beyond the scope of the study that aimed to evaluate the user-interface and most speciﬁcally the secondary focus which diﬀerentiates our approach from the common ﬁsh-eye techniques. In order not to have the user evaluate the underlying feature representation and the respective similarity metric, we modiﬁed the index for the experiment: Every time, the index was queried with an image with a ground truth annotation, the two most similar images from the respective topics were injected into the returned list of nearest neighbors. This ensured that the secondary would contain some relevant images. 6.2

Results

The user study was conducted with 30 participants – all of them graduate or post-graduate students. Their age was between 19 and 32 years (mean 25.5) and 40% were female. Most of the test persons (70%) were computer science students, with half of them having a background in computer vision or user interface design. 43% of the participants stated that they take photos on a regular basis and 30% use software for archiving and sorting their photo collection. The majority (77%) declared that they are open to new user interface concepts. Usability Comparison. Figure 9 shows the results from the questionnaire comparing the usability and helpfulness of the SL approach with baseline P&Z. What becomes immediately evident is that half of the participants rated the SL interface as being signiﬁcantly more helpful that the simple P&Z interface while being equally complicated in use. The intuitiveness of the SL was surprisingly rated slightly better than for the P&Z interface, which is an interesting outcome since we expected users to be more familiar with P&Z as it is more common in today’s user interfaces (e.g. Google Maps). This, however, suggests that interacting with a ﬁsh-eye lens can be regarded as intuitive for humans when interacting with large collections. The combination of both got even better ratings but has to be considered noncompetitive here, as it could have had an advantage by always being the last interface used. Participants have had time for getting used to the handling of the two complementary interfaces already. Moreover, since the collection did not change as for P&Z and SL, the combined interface might have had the advantage of being applied to a possibly easier collection – with

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helpfulness

simplicity

intuitivity

7

7

7

6

6

6

5

5

5

4

4

4

3

3

3

2

2

2

1

1 P&Z

SL

both

1 P&Z

SL

both

P&Z

SL

both

Fig. 9. Usability comparison of common panning & zooming (P&Z), adaptive SpringLens (SL) and the combination of both. Ratings were on a 7-point-scale where 7 is best. The box plots show minimum, maximum, median and quartiles for N = 30. Table 3. Percentage of marked images (N = 914) categorized by focus region and topic of the image in primary focus at the time of marking focus region

primary

ext. primary

secondary

none

same topic other topic no focus

37.75

4.27 4.49

30.74 13.24

4.38 2.08 3.06

total

37.75

8.75

43.98

9.52

topics being better distributed or a slightly better working similarity measure so that images of the same topic are found more easily. Usage of Secondary Focus. For this part, we restrict ourselves to the interaction with the last photo collection where both, P&Z and the lens, could be used and the participants had had plenty of time (approximately 15 to 30 minutes depending on the user) for practice. The question to be answered is, how much the users actually make use of the secondary focus which always contains some relevant images if the image in primary focus has a ground truth annotation.14 For each image marked by a participant, the location of the image at the time of marking was determined. There are four possible regions: primary focus (only the central image), extended primary focus (region covered by primary lens except primary focus image), secondary focus and the remaining region. Further, there are up to three cases for each region with respect to the (user-annotated or ground truth) topic of the image in primary focus. Table 3 shows the frequencies of the resulting eight possible cases. (Some combinations are impossible. E.g., the existence of a secondary focus implies some image in primary focus.) The most interesting number is the one referring to images in secondary focus that belong to the same topic as the primary because this is what the secondary focus 14

Ground truth annotation were never visible to the users.

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is supposed to bring up. It comes close to the percentage of the primary focus that – not surprisingly – is the highest. Ignoring the topic, (extended) primary and secondary almost contribute equally and only less than 10% of the marked images were not in focus – i.e. discovered only through P&Z. Emerging Search Strategies. For this part we again analyze only interaction with the combined interface. A small group of participants excessively used P&Z. They increased the initial thumbnail size in order to better perceive the depicted contents and chose to display all images as thumbnails. To reduce the overlap of thumbnails, they operated on a deeper zoom level and therefore had to pan a lot. The gaze data shows a tendency for systematic sequential scans which were however diﬃcult due to the scattered and irregular arrangement of the thumbnails. Further, some participants occasionally marked images not in focus because of being attracted by dominant colors (e.g. for the aquarium topic). Another typical strategy was to quickly scan through the collection by moving the primary focus – typically with small thumbnail size and at a zoom level that showed most of the collection but the outer regions. In this case the attention was mostly at the (extended) primary focus region with the gaze scanning in which direction to explore further and little to moderate attention at the secondary focus. Occasionally, participants would freeze the focus or slow down for some time to scan the whole display. In contrast to this rather continuous change of the primary focus, there was a group of participants that browsed the collection mostly by moving (in a single click) the primary focus to some secondary focus region – much like navigating an invisible neighborhood graph. Here, the attention was concentrated onto the secondary focus regions. User Feedback. Many participants had problems with an overcrowded primary ﬁsh-eye in dense regions. This was alleviated by temporarily zooming into the region which lets the images drift further apart. However, there are possibilities that require less interaction such as automatically spreading the thumbnails in focus with force-based layout techniques. Working on deeper zoom levels where only a small part of the collection is visible, the secondary focus was considered mostly useless as it was usually out of view. Further work could therefore investigates oﬀ-screen visualization techniques to facilitate awareness of and quick navigation to secondary focus regions out of view and better integrate P&Z and SL. The increasing “empty space” at deep zoom levels should be avoided – e.g. by automatically increasing the thumbnail size as soon as all thumbnails can be displayed without overlap. An optional re-arrangement of the images in view into a grid layout may ease sequential scanning as preferred by some users. Another proposal was to visualize which regions have already been explored similar to the (optionally time-restricted) “fog of war” used in strategy computer games. Some participants would welcome advanced ﬁltering options such as a prominent color ﬁlter. An undo function or reverse playback of focus movement would be desirable and could easily be implemented by maintaining a list of the last images in primary focus. Finally, some participants remarked that it would be nice to generate the secondary focus for a set of images (belonging to the same topic).

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In fact, it is even possible to adapt the similarity metric used for the nearest neighbor queries automatically to the task of ﬁnding more images of the same to topic as shown in recent experiments [49]. This opens an interesting research direction for future work.

7

Conclusion

A common approach for exploratory retrieval scenarios is to start with an overview from where the user can decide which regions to explore further. The focusadaptive SpringLens visualization technique described in this paper addresses the following three major problems that arise in this context: 1. Approaches that rely on dimensionality reduction techniques to project the collection from high-dimensional feature space onto two dimensions inevitably face projection errors: Some tracks will appear closer than they actually are and on the other side, some tracks that are distant in the projection may in fact be neighbors in the original space. 2. Displaying all tracks at once becomes infeasible for large collections because of limited display space and the risk of overwhelming the user with the amount of information displayed. 3. There is more than one way to look at a music collection – or more speciﬁcally to compare two music pieces based on their features. Each user may have a diﬀerent way and a retrieval system should account for this. The ﬁrst problem is addressed by introducing a complex distortion of the visualization that adapts to the user’s current region of interest and temporarily alleviates possible projection errors in the focused neighborhood. The amount of displayed information can be adapted by the application of several sparser ﬁlters. Concerning the third problem, the proposed user-interface allows users to (manually) adapt the underlying similarity measure used to compute the arrangement of the tracks in the projection of the collection. To this end, weights can be speciﬁed that control the importance of diﬀerent facets of music similarity and further an aggregation function can be chosen to combine the facets. Following a user-centered design approach with focus on usability, a prototype system has been created by iteratively alternating between development and evaluation phases. For the ﬁnal evaluation, an extensive user study including gaze analysis using an eye-tracker has been conducted with 30 participants. The results prove that the proposed interface is helpful while at the same time being easy and intuitive to use.

Acknowledgments This work was supported in part by the German National Merit Foundation, the German Research Foundation (DFG) under the project AUCOMA, and the European Commission under FP7-ICT-2007-C FET-Open, contract no. BISON211898. The user study was conducted in collaboration with Christian Hentschel

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who also took care of the image feature extraction. The authors would further like to thank all testers and the participants of the study for their time and valuable feedback for further development, Tobias Germer for sharing his ideas and code of the original SpringLens approach [9], Sebastian Loose who has put a lot of work into the development of the ﬁlter and zoom components, the developers of CoMIRVA [40] and JAudio [28] for providing their feature extractor code and George Tzanetakis for providing insight into his MIREX ’07 submission [52]. The Landmark MDS algorithm has been partly implemented using the MDSJ library [1].

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A Database Approach to Symbolic Music Content Management Philippe Rigaux1 and Zoe Faget2 1

2

Cnam, Paris [email protected] Lamsade, Univ. Paris-Dauphine [email protected]

Abstract. The paper addresses the problem of content-based access to large repositories of digitized music scores. We propose a data model and query language that allow an in-depth management of musical content. In order to cope with the ﬂexibility of music material, the language is designed to easily incorporate user-deﬁned functions at early steps of the query evaluation process. We describe our architectural vision, develop a formal description of the language, and illustrate a user-friendly syntax with several classical examples of symbolic music information retrieval. Keywords: Digital Libraries, Data Model, Time Series, Musicological Information Management.

1

Introduction

The presence of music on the web has grown exponentially over the past decade. Music representation is multiple (audio ﬁles, midi ﬁles, printable music scores. . . ) and is easily accessible through numerous platforms. Given the availability of several compact formats, the main representation is by means of audio ﬁles, which provide immediate access to music content and are easily spreaded, sampled and listened to. However, extracting structured information from an audio ﬁle is a diﬃcult (if not impossible) task, since it is submitted to the subjectivity of interpretation. On the other hand, symbolic music representation, usually derived from musical scores, enables exploitation scenarios diﬀerent from what audio ﬁles may oﬀer. The very detailed and unambiguous description of the music content is of high interest for communities of music professionals, such as musicologists, music publishers, or professional musicians. New online communities of users arise, with an interest in a more in-depth study of music than what average music lovers may look for. The interpretation of the music content information (structure of musical pieces, tonality, harmonic progressions. . . ) combined with meta-data (historic and geographic context, author and composer names. . . ) is a matter of human expertise. Speciﬁc music analysis tools have been developped by music professionals for centuries, and should now be scaled to a larger level in order to provide scientiﬁc and eﬃcient analysis of large collections of scores. S. Ystad et al. (Eds.): CMMR 2010, LNCS 6684, pp. 303–320, 2011. c Springer-Verlag Berlin Heidelberg 2011

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Another fundamental need of such online communities, this time shared with more traditional platforms, is the ability to share content and knowledge, as well as annotate, compare and correct all this available data. This Web 2.0 space with user generated content helps improve and accelerate research, with the added bonus to make available to a larger audience sources which would otherwise remain conﬁdential. Questions of copyright, security and controled contributions are part of the social networks problematic. To summarize, a platform designed to be used mainly, but not only, by music professionals, should oﬀer classic features such as browsing and rendering, but also the ability to upload new content, annotate partitions, search by content (exact and similarity search), and several tools of music content manipulation and analysis. Most of the proposals devoted so far to analysis methods or similarity searches on symbolic music focus on the accuracy and/or relevancy of the result, and implicitly assume that these procedures apply to a small collection [2,3,13,16]. While useful, this approach gives rise to several issues when the collection consists of thousands of scores, with heterogeneous descriptions. A ﬁrst issue is related to software engineering and architectural concerns. A large score digital library provides several services to many diﬀerents users or applications. Consistency, reliability, and security concerns call for the deﬁnition of a single consistent data management interface for these services. In particular, one can hardly envisage the publication of ad-hoc search procedures that merely exhibit the bunch of methods and algorithms developed for each speciﬁc retrieval task. The multiplication of these services would quickly overwhelm external users. Worse, the combination of these functions, which is typically a diﬃcult matter, would be left to external applications. In complex systems, the ability to compose ﬂuently the data manipulation operators is a key to both expressive power and computational eﬃciency. Therefore, on-line communities dealing with scores and/or musical content are often limited either by the size of their corpus or the range of possible operations, with only one publicized strong feature. Examples of on-line communities include Mutopia [26], MelodicMatch [27] or Musipedia [28]. Wikifonia [29] oﬀers a wider range of services, allowing registered users to publish and edit sheet music. One can also cite the OMRSYS platform described in [8]. A second issue pertains to scalability. With the ongoing progress in digitization, optical recognition and user content generation, we must be ready to face an important growth of the volume of music content that must be handled by institutions, libraries, or publishers. Optimizing accesses to large datasets is a delicate matter which involves many technical aspects that embrace physical storage, indexing and algorithmic strategies. Such techniques are usually supported by a specialized data management system which releases applications from the burden of low-level and intricate implementation concerns. To our knowledge, no system able to handle large heterogeneous Music Digital Libraries while smoothly combining data manipulation operators exists at this moment. The HumDrum toolkit is a widely used automated musicological

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analysis tool [14,22], but representation remains at a low level. A HumDrum based system will lack in ﬂexibility and will depend too much on how ﬁles are stored. This makes diﬃcult the development of indexing of optimization techniques. Another possible approach would be a system based on MusicXML, an XML based ﬁle format [12,24]. It has been suggested recently that XQuery may be used over MusicXML for music queries [11], but XQuery is a general-purpose query language which hardly adapts to the speciﬁcs of symbolic music manipulation. Our objective in this paper is to lay the ground for a score management system with all the features of a Digital Scores Library combined with content manipulation operators. Among other things, a crucial component of such a system is a logical data model speciﬁcally designed for symbolic music management and its associated query language. Our approach is based on the idea that the management of structured scores corresponds, at the core level, to a limited set of fundamental operations that can be deﬁned and implemented once for all. We also take into account the fact that the wide range of user needs calls for the ability to associate these operations with user-deﬁned functions at early steps of the query evaluation process. Modeling the invariant operators and combining them with user-deﬁned operations is the main goal of our design eﬀort. Among numerous advantages, this allows the deﬁnition of a stable and robust query() service which does not need ad-hoc extensions as new requirements arrive. We do not claim (yet) that our model and its implementation will scale easily, but a high level representation like our model is a pre-requisit in order to allow the necessary ﬂexibility for such futur optimization. Section 3.2 describes in further details the Neuma platform, a Digital Score Library [30] devoted to large collections of monodic and polyphonic music from the French Modern Era (16th – 18th centuries). One of the central piece of the architecture is the data model that we present in this paper. The language described in section 5 oﬀers a generic mechanism to search and transform music notation. The rest of this paper ﬁrst discusses related work (Section 2). Section 3 presents the motivation and the context of our work . Section 4 then exposes the formal fundations of our model. Section 6 concludes the paper.

2

Related Work

The past decade has witnessed a growing interest in techniques for representing, indexing and searching (by content) music documents. The domain is commonly termed “Music Information Retrieval” (MIR) although it covers many aspects beyond the mere process of retrieving documents. We refer the reader to [19] for an introduction. Systems can manipulate music either as audio ﬁles or in symbolic form. The symbolic representation oﬀers a structured representation which is well suited for content-based accesses, sophisticated manipulations, and analysis [13]. An early attempt to represent scores as structured ﬁles and to develop search and analysis functions is the HumDrum format. Both the representation and

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the procedures are low-level (text ﬁles, Unix commands) which make them difﬁcult to integrate in complex application. Recent works try to overcome these limitations [22,16]. Musipedia proposes several kinds of interfaces to search the database by content. MelodicMatch is a similar software analysing music through pattern recognition, enabling search for musical phrases in one or more pieces. MelodicMatch can search for melodies, rhythms and lyrics in MusicXML ﬁles. The computation of similarity between music fragments is a central issue in MIR systems [10]. Most proposal focus on comparisons of the melodic proﬁles. Because music is subject to many small variations, approximate search is of order, and the problem is actually that of ﬁnding nearest neighbors to a given pattern. Many techniques have been experimented, that vary depending on the melodic encoding and the similarity measure. See [9,4,1,7] for some recent proposals. The Dynamic Time Warping (DTW) distance is a well-known popular measure in speech recognition [21,20]. It allows the non-linear mapping of one signal to another by minimizing the distance between the two. The DTW distance is usually chosen over the less ﬂexible Euclidian distance for time series alignment [5]. The DTW computation is rather slow but recent works show that it can be eﬃciently indexed [25,15]. We are not aware of any general approach to model and query music notation. A possible approach would be to use XQuery over MusicXML documents as suggested in [11]. XQuery is a general-purpose query language, and its use for music scores yields complicated expressions, and hardly adapts to the speciﬁcs of the objects representation (e.g., temporal sequences). We believe that a dedicated language is both more natural and more eﬃcient. The temporal function approach outlined here can be related to time series management [17].

3 3.1

Architecture Approach Overview

Figure 1 outlines the main components of a Score Management System built around our data model. Basically, the architecture is that of a standard DBMS,

Symbolic Music Application

Large−scale score management system

query results

queries

Symb. music model User functions

User QL

Query algebra

Time series manipulation primitives Storage and indexing Fig. 1. Approach overview

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and we actually position our design as an extension of the relational model, making it possible to incorporate the new features as components of an extensible relational system. The model consists of a logical view of score content, along with a user query language and an algebra that operates in closed form (i.e., each operator consumes and produces instances of the model). The algebra can be closely associated with a library of user-deﬁned functions which must be provided by the application and allow to tailor the query language to a speciﬁc domain. Functions in such a library must comply to constraints that will be developed in the next section. This approach brings, to the design and implementation of applications that deal with symbolic music, the standard and well-known advantages of specialized data management systems. Let us just mention the few most important: (i) ability to rely on a stable, well-deﬁned and expressive data model, (ii) independence between logical modeling and physical design, saving the need to confront programmers with intricate optimization issues at the application level and (iii) eﬃciency of set-based operators and indexes provided by the data system. Regarding the design of our model, the basic idea is to extend the relational approach with a new type of attribute : the time series type. Each such attribute represents a (peculiar) temporal function that maps a discrete temporal space to values in some domain. The model supports the representation of polyphonic pieces composed of “voices”, each voice being a sequence of “events” in some music-related domain (notes, rests, chords) such that only one event occurs at a given instant. Adding new domains allows to extend the concept to any sequence of symbols taken from a ﬁnite alphabet. This covers monodies, text (where the alphabet consists of syllables) as well as, potentially, any sequence of music-related information (e.g., ﬁngerings, performance indications, etc.). We model a musical piece as Synchronized Time Series (STS). Generally speaking, a time series is an ordered sequence of values taken at equal time intervals. Some of the traditional domains that explicitly produce and exploit time series are, among others, sales forecasting, stock market analysis, process and quality control, etc. [6]. In the case of music notation, “time” is not to be understood as a classic calendar (i.e., days or timestamps) as in the previously mentioned examples but rather, at a more abstract level, as a sequence of events where the time interval is the smallest note duration in the musical piece. Consider now a digital library that stores music information and provides query services on music collections. A music piece consists of one or several parts which can be modelled as time series, each represented by a score. Fig. 2 is a simple example of a monodic piece, whereas polyphonic music pieces (Fig. 3) exhibit a synchronization of several parts. The temporal domain of interest here is an abstract representation if the temporal features of the times series. Essentially, these features are (i) the order of the musical “events” (notes, rests), (ii) the relative duration of the events, and (iii) the required synchronization of the diﬀerent parts (here lyrics and notes).

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Fig. 2. A monodic score

Fig. 3. A polyphonic score

Here are a few examples of queries: 1. Get the scores whose lyrics contains the word ’conseil’ (selection) 2. Get the melodic part that corresponds to the word ’conseil’ (selection and temporal join) 3. Find a melodic pattern. (search by similarity) When a collection of musical score is to be studied, queries regarding common features are also of interest. For example 1. Find all musical pieces in the collection starting with the note C and getting to G in 5 times unit. 2. Find if minor chords occur more often than major chord synchronized with the word ’tot’ in Bach’s work. We provide an algebra that operates in closed form over collections of scores. We show that this algebra expresses usual operations (e.g., similarity search), achieves high expressiveness through unbounded composition of operators and natively incorporates the ability to introduce user-deﬁned function in query expressions, in order to meet the needs of a speciﬁc approach to symbolic music manipulation. Finally, we introduce a user-friendly query language to express algebraic operations. We believe that our approach deﬁnes a sound basis to the implementation of a specialized query language devoted to score collections management at large scale. In the next section, we describe the Neuma plateform, a score management system built around those concepts. 3.2

Description of the Neuma Platform

The Neuma platform is a digital library devoted to symbolic music content. It consists of a repository dedicated to the storage of large collections of digital scores, where users/applications can upload their documents. It also proposes a family of services to interact with those scores (query, publish, annotate, transform and analyze).

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The Neuma platform is meant to interact with distant web applications with local database that store corpus-speciﬁc informations. The purpose of Neuma is to manage all music content informations and leave contextual data to the client application (author, composer, date of publication, . . . ). To send a new document, an application calls the register() service. So far only MusicXML documents can be used to exchange musical description, but any format could be used provided the corresponding mapping function is in place. Since MusicXML is widely used, it is suﬃcient for now. The mapping function extracts a representation of the music content of the document which complies to our data model. To publish a score -wether it’s a score featured in the database or a modiﬁed one- the render() service is called. The render() service is based on the Lilypond package. The generator takes an instance of our model as input and converts it into a Lilypond ﬁle. The importance of a uniﬁed data model appears clearly in such an example : the render service is based on the model, making it rather easy to visualize a transformed score, when it would be a lot more diﬃcult to do so if it was instead solely based on the document format. A large collection of scores would be useless if there was no appropriate query() service allowing reliable search by content. As explained before, the Neuma digital library stores all music content (originated from diﬀerent collections, potentially with heterogenous description) in its repository and leaves descriptive contextual data speciﬁc to collections in local databases. Regardless of their original collection, music content complies to our data model so that it can be queried accordingly. Several query types are oﬀered : exact, transposed, with or without rythm, or contour which only takes in account the shape of the input melody. The query() service combines content search with descriptive data. A virtual keyboard is provided to enter music content, and research ﬁelds can be ﬁlled to adress the local databases. The Neuma platform also provides and annotate() service. The annotations are a great way to enrich the digital library and make sure it keeps growing and improving. In order to use the annotate() service one ﬁrst selects part of a score (a set of elements of the score) and enters information about this portion. There are diﬀerent kinds of annotations : free text (useful for performance indications), or pre-selected terms from an ontology (for identifying music fragments). Annotations can be queried alongside other criterias previously mentioned.

4 4.1

The Data Model Preliminaries

A musical domain dommusic is a product domain combining heterogenous musical informations. For example, the domain of a simple monodic score is dommusic = dompitch × domrythm . Any type of information that can be extracted from symbolic music (such as measures, alterations, lyrics. . . ) can be added to the domain. Each domain contains two distinguished values: the neutral value and the null value ⊥. The

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Boolean operations ∧ (conjunction) and ∨ (disjunction) verify, for any a ∈ dom, a ∧ ⊥ = ⊥, a ∧ = a and a ∨ ⊥ = a ∨ = a. In some cases, ⊥ and can also be viewed as false and true. With a given musical domain comes a set of operations provided by the user and related to this speciﬁc domain. When managing a collections of Choir parts, a function such as max() which computes the highest pitch is meaningful, and becomes irrelevant when managing of collection of Led Zeppelin tablatures. The operators designed to single out each part of a complex music domain are presented in the extended relational algebra. We subdivise the time domain T into a deﬁned, regular, repeated pattern. The subdivision of time is the smallest interval between two musical events. The time domain T is then a countable ordered set isomorphic to N. We introduce a set of internal time functions, designed to operate on the time domain T . We deﬁne as L the class of functions from T to T otherwise known as internal time functions (ITF). Any L can be used to operate on the time domain as long as the user ﬁnds this operation meaningful. An important sub-class of L is the set of linear functions of the form t → nt + m. We denote them temporal scaling functions in what follows, and further distinguish the families of warping functions of the form warpm : T → T , t → mt and shifting functions shif tn : T → T , t → t + n. Shifting functions are used to ignore the ﬁrst n events of a time series, while warping functions single out one out of m events. A musical time series (or voice) is a mapping from a time domain T into a musical domain dommusic . When dealing with a collection of scores sharing a number of common properties, we introduce the schema of a relation. The schema distinguishes atomic attributes names (score_id, author, year. . . ) and times series (or voices) names. We denote by TS([dom]) the type of these attributes, where [dom] is the domain of interest. Here is, for example, the schema of a music score Score(Id : int, Composer : string, V oice : TS(vocal), P iano : TS(polyMusic)).

Note that the time domain is shared by the vocal part and the piano part. For the same score, one could have made the diﬀerent choice of schema where vocal and piano are represented in the same time series of type TS([vocal × polyMusic]) The domain vocal adds the lyrics domain to the classic music domain: domvocals = dompitch × domrythm × domlyrics . The domain polymusic is the product dompolymusic = (dompitch × domrythm )N . We will now deﬁne two sets of operators gathered into two algebras: the (extended) relational algebra, and the times series algebra.

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311

The Relational Algebra Alg(R)

The Alg(R) algebra consists of the usual operators selection σ, product ×, union ∪ and diﬀerence −, along with an extended projection Π. We present simple examples of those operators. Selection, σ. Select all scores composed by Louis Couperin: σauthor= Louis

Couperin (Score)

Select all scores containing the lyrics ’Heureux’: σlyrics(voice)⊃ Heureux

Seigneur (Score)

Projection, π. We want the vocal parts from the Score schema without the piano part. We project the piano out: πvocals (Score) Product, ×. Consider a collection of duets, split into the individual vocal parts of male and female singers, with the following schemas M ale_P art(Id : int, V oice : TS(vocals)), F emale_P art(Id : int, V oice : TS(vocals)). To get the duet scores, we cross product a female part and a male part. We get the relation Duet(Id : int, M ale_V : TS(vocals), F emale_V : TS(vocals)). Note that the time domain is implicitly shared. In itself, the product doesn’t have much interest, but together with the selection operator it becomes the join operators 1. In the previous example, we shouldn’t blindly associate any male and female vocal parts, but only the ones sharing the same Id. σM.Id=F.Id (M ale × F emale) ≡ M ale 1Id=Id F emale. Union ∪. We want to join the two consecutive movements of a piece which have two separate score instances. Score = Scorept1 ∪ Scorept2 . The time series equivalent of the null attribute is the empty time series, for which each event is ⊥. Beyond classical relational operators, we introduce an emptyness test ∅? that operates on voices and is modeled as: ∅? (s) = f alse if ∀t, s(t) = ⊥, else ∅? (s) = true. The emptynes test can be introduced in selection formulas of the σ operator.

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Consider once more the relation Score. We want to select all scores featuring the word ’Ave’ in the lyrics part of V. We need a user function m : (lyrics) → (⊥, ) such that m( Ave ) = , else ⊥. Lyrics not containing ’Ave’ are transformed into the “empty” time series t → ⊥, ∀t. The algebraic expression is: σ∅? (W ) (Π[Id,V,W :m(lyrics(V ))] (Score)). 4.3

The Time Series Algebra Alg(T S)

We now present the operators of the time series algebra Alg(T S) (◦, ⊕, A). Each operator takes one or more time series as input and produces a time series. This way, operating in closed form, operators can be composed. They allow, in particular: alteration of the time domain in order to focus on speciﬁc instants (external composition); apply a user function to one or more times series to form a new one (addition operator); windowing of time series fragments for matching purposes (aggregation). In what follows, we take an instance s of the Score schema to run several examples. The external composition ◦ composes a time series s with an internal temporal function l. Assume our score s has two movements, and we only want the second one. Let shif tn be an element of the shift family functions parametrized by a constant n ∈ N. For any t ∈ T , s◦shif tn(t) = s(t+n). In other words, s◦shif tn is the time series extracted from s where the ﬁrst n events are ignored. We compose s with the shif tL function, where L is the length of the ﬁrst movement, and the resulting time series s ◦ shif tL is our result. Imagine now that we want only the ﬁrst note of every measure. Assuming we are in 4/4 and the time unit is one fourth, we compose s with warp16 . The time series s ◦ warp16 is the time series where only one out of sixteen events are considered, therefore the ﬁrst note of every measure. We now give an example of the addition operator ⊕. Let dompitch be the domain of all musical notes and domint the domain of all musical intervals. We can deﬁne an operation from dompitch × dompitch to domint , called the harm operator, which takes two notes as input and computes the interval between these two notes. Given two time series representing each a vocal part, for instance V1 =soprano, V2 =alto, we can deﬁne the time series V1 ⊕harm V2 of the harmonic progression (i.e., the sequence of intervals realized by the juxtaposition of the two voices). Last, we present the aggregation mechanism A. A typical operation that we cannot yet obtained is the “windowing” which, at each instant, considers a local part of a voice s and derives a value from this restricted view. A canonical example is pattern matching. So far we have no generic way to compare a pattern P with all subsequences of a time series s. The intuitive way to do pattern matching is to build all subsequences from s and compare P with each of them,

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dom

dom

γ s(τ ) ∈ dom

s(t)

s ◦ λτ −10

γ s ◦ λτ

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dom γ

aggregation step s ◦ λτ +10 derivation step

t

τ a. Function s(t)

τ − 10

τ

τ + 10

t

b. Sequence of derived functions dλ (s)

Fig. 4. The derivation/aggregation mechanism

using an appropriate distance. This is what the aggregation mechanism does, in two steps: 1. First, we take a family of internal time functions λ such that for each instant τ , λ(τ ) is an internal time function. At each instant τ , a TS sτ = dλ s(τ ) = s ◦ λ(τ ) is derived from s thanks to a derivation operator dλ ; 2. Then a user aggregation function γ ∈ Γ is applied to the TS sτ , yielding an element from dom. Fig. 4 illustrates this two-steps process. At each instant τ a new function is derived. At this point, we obtain a sequence of time series, each locally deﬁned with respect to an instant of the time axis. This sequence corresponds to local views of s, possibly warped by temporal functions. Note that this intermediate structure is not covered by our data model. The second step applies an aggregation function γ. It takes one (or several, depending on the arity of γ) derived series, and produces an element from dom. The combination of the derivation and aggregation steps results in a time series that complies to the data model. To illustrate the derivation step, we derive our score s with respect to the shift function : dshif t (s) = (s ◦ shif t0 , s ◦ shif t1 , . . . , s ◦ shif tn ). The aggregation step takes the family of time series obtained with the derivation step and applies a user function to all of them. For our ongoing example, this translates to applying the user function DT WP (which computes the DTW distance between the input time series s and the pattern P ) to dshif t (s). We denote this two steps procedure by the following expression: ADTWP ,shift (s). 4.4

Example: Pattern Matching Using the DTW Distance

To end this section, we give an extended example using operators from Alg(R) and Alg(T S) . We want to compute, given a pattern P and a score, the instant

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t where the Dynamic Time Warping (DTW) distance between P and V is less than 5. First, we compute the DTW distance between P and the voice V1 of a score, at each instant, thanks to the following Alg(T S) expression: e = AdtwP ,shift (V1 ) where dtwP is the function computing the DTW distance between a given time series and the pattern P . Expression e deﬁnes a time series that gives, at each instant α, the DTW distance between P and the sub-series of V1 that begins at α. A selection (from Alg(T S) ) keeps the values in e below 5, all others being set to ⊥. Let ψ be the formula that expresses this condition. Then: e = σψ (e). Finally, expression e is applied to all the scores in the Score relation with the Π operator. An emptyness test can be used to eliminate those for which the DTW is always higher that 5 (hence, e is empty). σ∅? (e ) (Π[composer,V1 ,V2 ,e ] (Score)).

5

User Query Language

The language should have a precise semantic so that the intend of the query is unambiguous. It should specify what to be done, and not how to do it. The language should understand diﬀerent kind of expressions: queries as well as deﬁnition of user functions. Finally, the query syntax should be easily understandable by a human reader. 5.1

Overview

The language is implemented in Ocaml. Time series are represented by lists of array of elements, where elements can be integers, ﬂoat, string, booleans, or any type previously deﬁned. This way we allow ourselves to synchronize voices of diﬀerent types. The only restriction is that we do not allow an element to be a time series. We deﬁne the type time series ts_t by a couple (string*element), where string is the name of the voice, allowing fast access when searching for a speciﬁc voice. 5.2

Query Structure

The general structure of a query is: from Table [alias] let NewAttribute := map (function, Attribute) construct Attribute | NewAttribute where Attribute | NewAttribute = criteria

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The from clause should list at least one table from the database, and the alias is optional. The let clause is optional. There can be as many let clause as desired. Voices modiﬁed in a let clause are from attributes present in tables listed in the from clause. The construct clause should list at least one attribute, either from one the listed tables in the from clause, or a modiﬁed attribute from a let clause. The where clause is optional. It consists of a list of predicates, connected by logical operators (And, Or, Not). 5.3

Query Syntax

The query has four main clauses : from, let, construct, where. In all that follows, by attribute we mean both classical attributes or time series. Attributes which appear in the construct, let or where clauses are called either ColumnName if there is no ambiguity or TableName.ColumnName otherwise. If the attribute is a time series and we want to refer to a speciﬁc voice, we project by using the symbol ->. Precisely, a voice is called ColumnName>VoiceName or TableName.ColumnName->VoiceName. The from clause enumerate a list of tables from the database, with an optional alias. The table names should refer to actual tables of the database. Aliases should not be duplicated, nor should they be the name of an existing table. The optional let clause apply a user function to an attribute. The attribute should be from one of the tables listed in the from clause. When the attribute is a time series, this is done by using map. When one wants to apply a binary operator on two time series, we use map2. The construct clause lists the names of the attributes (or modiﬁed attributes) which should appear in the query result. The where clause evaluates a condition on an attribute or a modiﬁed attribute introduced by the let clause. If the condition is evaluated to true, then the line it refers to is considered to be a part of the result query. Lack of a where clause is evaluated as always true. The where clause supports the usual arithmetic (+, −, ∗, /), logical (And, Or, Not) and comparison operators (=,<>, >,<,>=, <=, contains) which are used inside predicates. 5.4

Query Evaluation

A query goes through several steps so that the instructions it contains can be processed. A query can be simple (retrieve a record from the database) or complex with content manipulation and transformation. The results (if any) are then returned to the user. Query analysis. The expression entered by the user (either a query or a user deﬁned function) is analyzed by the system. The system veriﬁes the query’s syntax. The query is turned into an abstract syntax tree in which each node is an algebraic operation (from bottom to top :

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set of tables (from), selection (where), content manipulation (let), projection (construct)). Query evaluation. The abstract syntax tree is then transformed into an evaluation tree. This steps is a ﬁrst simple optimisation, where table names become pointers to the actual tables and column names are turned into the column’s indice. This step also veriﬁes the validity of the query: existence of tables in the from clause, unambiguity of the columns in the where clause, consistency of the attributes in the construct clause, no duplicate aliases. . . Query execution. In this last step, the nodes of the evaluation tree call the actual algorithms, so that the program computes the result of the query. The bottom node retrieves the ﬁrst line of the table in the from clause. The second node access the column featured in the where clause, and evaluates the selection condition. If the condition is true, then the next node is called, which print the corresponding result on the output chanel, then returns to the ﬁrst node and iterate the process on the next line. If the condition is evaluated as false, then the ﬁrst node is called again and the process is iterated on the next line. The algorithm ends when there are no more lines to evaluate. 5.5

Algebraic Equivalences

In this section, we give the syntaxic equivalent to all algebraic operators previously introduced in section 4, along with some examples. Relational operators – Selection algebraic notation : σF (R), where F is a formula, R a relation. syntaxic equivalent : where. . . = – Projection algebraic notation : ΠA (R), where A is a set of attributes, R a relation syntaxic equivalent : construct Example : the expression Πid,voice (σcomposer= Faure (Psalms)) is equivalent to from Score construct id, voice where composer=’Faure’ Extension to time series – Projection algebraic notation : ΠV (S), where V is a set of voices, S a time series syntaxic equivalent : S− > (voice 1 , . . . , voice n )

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– Selection algebraic notation : σF (V ), where F is a formula, V is a set of voices of a time series S. syntaxic equivalent : where S->V . . . contains Example : the expression Πid,Π

pitch,rythm (voice)

(σΠlyrics (voice)⊃ Heureux les hommes ,composer = Faure (Psalms))

is equivalent to from construct where and

Score id, voice->(pitch,rythm) voice->lyrics contains ’Heureux les hommes’ composer = ’Faure’

Remark. if the time series consists of several synchronized voices, contains shoud list as many conditions as voices. Example: where voice->(lyrics,pitch) contains (’Heureux les hommes’, ’A3, B3,B3’)). – Product algebraic notation : s × t, where s and t are two time series. syntaxic equivalent : synch Example : the expresison ΠM.V oice×F.V oice (σM.Id=F.Id (M ale × F emale)) is equivalent to from let construct where

Male M, Female F $duet := synch(M.Voice,F.Voice) $duet M.id=F.id

Time series operator – Addition algebraic notation : ⊕op , where op is a user function. syntaxic equivalent : map and map2 Examples : • the expression Πpitch(voice)⊕ ( Psalms) transpose(1) is equivalent from let construct

to Psalms $transpose := map(transpose(1), voice->pitch) $transpose

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• the expression Πtrumpet(voice)⊕

harm clarinet(voice)

(Duets)

is equivalent to from Duets let $harmonic_progression := map2(harm, trumpet->pitch, clarinet->pitch) construct $harmonic_progression – Composition algebraic notation : S ◦ γ where γ is an internal temporal function and S a time series. syntaxic equivalent : comp(S, γ) – Aggregation - derivation algebraic notation : Aλ,Γ (S), where S is a time series, Γ is a family of internal time function and λ is an agregation function syntaxic equivalent : derive(S, Γ, λ). The family of internal time functions Γ is a mapping from the time domain into the set of internal time functions. Precisely, for each instant n, Γ (n) = γ, an internal time function. The two mostly used family of time functions Shift and Warp are provided. Example the expression Πid,Adtw(P),shift (Psalm) is equivalent to from let construct

6

Psalm $dtwVal := derive(voice, Shift, dtw(P)) id, $dtwVal

Conclusion and Ongoing Work

By adopting from the beginning an algebraic approach to the management of time series data sets, we directly enable an expressive and stable language that avoids a case-by-case deﬁnition of a query language based on the introduction of ad-hoc functions subject to constant evolution. We believe that this constitutes a sound basis for the development of applications that can rely on an expressive and eﬃcient data management. Current eﬀorts are being put in language implementation in order to optimize query evaluation. Our short-term roadmap also includes an investigation of indexing structures apt at retrieving patterns in large collections. Acknowledgments. This work is partially supported by the French ANR Neuma project, http://neuma.irpmf-cnrs.fr. The authors would like to thank Virginie Thion-Goasdoue and David Gross-Amblard.

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References 1. Allan H., Müllensiefen D. and Wiggins,G.A.: Methodological Considerations in Studies of Musical Similarity. In: Proc. Intl. Society for Music Information Retrieval (ISMIR) (2007) 2. Anglade, A. and Dixon, S.: Characterisation of Harmony with Inductive Logic Programming. In: Proc. Intl. Society for Music Information Retrieval (ISMIR) (2008) 3. Anglade, A. and Dixon, S.: Towards Logic-based Representations of Musical Harmony for Classiﬁcation Retrieval and Knowledge Discovery. In: MML (2008) 4. Berman, T., Downie, J., Berman, B.: Beyond Error Tolerance: Finding Thematic Similarities in Music Digital Libraries. In: Proc. European. Conf. on Digital Libraries, pp. 463–466 (2006) 5. Berndt, D., Cliﬀord, J.: Using dynamic time warping to ﬁnd patterns in time series. In: AAAI Workshop on Knowledge Discovery in Databases, pp. 229–248 (1994) 6. Brockwell, P.J., Davis, R.: Introduction to Time Series and forecasting. Springer, Heidelberg (1996) 7. Cameron, J., Downie, J.S., Ehmann, A.F.: Human Similarity Judgments: Implications for the Design of Formal Evaluations. In: Proc. Intl. Society for Music Information Retrieval, ISMIR (2007) 8. Capela, A., Rebelo, A., Guedes, C.: Integrated recognition system for music scores. In: Proc. of the 2008 Internation Computer Music Conferences (2008) 9. Downie, J., Nelson, M.: Evaluation of a simple and eﬀective music information retrieval method. In: Proc. ACM Symp. on Information Retrieval (2000) 10. Downie, J.S.: Music Information Retrieval. Annual review of Information Science and Technology 37, 295–340 (2003) 11. Ganseman, J., Scheunders, P., D’haes, W.: Using XQuery on MusicXML Databases for Musicological Analysis. In: Proc. Intl. Society for Music Information Retrieval, ISMIR (2008) 12. Good, M.: MusicXML in practice: issues in translation and analysis. In: Proc. 1st Internationl Conference on Musical Applications Using XML, pp. 47–54 (2002) 13. Haus, G., Longari, M., Pollstri, E.: A Score-Driven Approach to Music Information Retrieval. Journal of American Society for Information Science and Technology 55, 1045–1052 (2004) 14. Huron, D.: Music information processing using the HumDrum toolkit: Concepts, examples and lessons. Computer Music Journal 26, 11–26 (2002) 15. Keogh, E.J., Ratanamahatana, C.A.: Exact Indexing of Dynamic Time Warping. Knowl. Inf. Syst. 7(3), 358–386 (2003) 16. Knopke, I. : The Perlhumdrum and Perllilypond Toolkits for Symbolic Music Information Retrieval. In: Proc. Intl. Society for Music Information Retrieval (ISMIR) (2008) 17. Lee, J.Y., Elmasri, R.: An EER-Based Conceptual Model and Query Language for Time-Series Data. In: Proc. Intl.Conf. on Conceptual Modeling, pp. 21–34 (1998) 18. Lerner, A., Shasha, D.: A Query : Query language for ordered data, optimization techniques and experiments. In: Proc. of the 29th VLDB Conference, Berlin, Germany (2003) 19. Muller, M.: Information Retrieval for Music and Motion. Springer, Heidelberg (2004) 20. Rabiner, L., Rosenberg, A., Levinson, S.: Considerations in dynamic time warping algorithms for discrete word recognition. IEEE Trans. Acoustics, Speech and Signal Proc. ASSP-26, 575–582 (1978)

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21. Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoustics, Speech and Signal Proc. ASSP-26, 43– 49 (1978) 22. Sapp, C.S.: Online Database of Scores in the Humdrum File Format. In: Proc. Intl. Society for Music Information Retrieval, ISMIR (2005) 23. Typke, R., Wiering, F., Veltkamp, R.C.: A Survey Of Music Information Retrieval Systems. In: Proc. Intl. Society for Music Information Retrieval, ISMIR (2005) 24. Viglianti, R.: MusicXML : An XML based approach to automatic musicological analysis. In: Conference Abstracts of the Digital Humanities (2007) 25. Zhu, Y., Shasha, D.: Warping Indexes with Envelope Transforms for Query by Humming. In: Proc. ACM SIGMOD Symp. on the Management of Data, pp. 181– 192 (2003) 26. Mutopia, http://www.mutopiaproject.org (last viewed February 2011) 27. Melodicmatch, http://www.melodicmatch.com (last viewed February 2011) 28. Musipedia, http://www.musipedia.org (last viewed February 2011) 29. Wikifonia, http://www.wikifonia.org (last viewed February 2011) 30. Neuma, http://neuma.fr (last viewed February 2011)

Error-Tolerant Content-Based Music-Retrieval with Mathematical Morphology Mikko Karvonen, Mika Laitinen, Kjell Lemstr¨ om, and Juho Vikman University of Helsinki Department of Computer Science [email protected] {mika.laitinen,kjell.lemstrom,juho.vikman}@helsinki.fi http://www.cs.helsinki.fi

Abstract. In this paper, we show how to apply the framework of mathematical morphology (MM) in order to improve error-tolerance in contentbased music retrieval (CBMR) when dealing with approximate retrieval of polyphonic, symbolically encoded music. To this end, we introduce two algorithms based on the MM framework and carry out experiments to compare their performance against well-known algorithms earlier developed for CBMR problems. Although, according to our experiments, the new algorithms do not perform quite as well as the rivaling algorithms in a typical query setting, they provide ease of adjusting the desired error tolerance. Moreover, in certain settings the new algorithms become even faster than their existing counterparts. Keywords: MIR, music information retrieval, mathematical morphology, geometric music retrieval, digital image processing.

1

Introduction

The snowballing number of multimedia data and databases publicly available for anyone to explore and query has made the conventional text-based query approach insuﬃcient. To eﬀectively query these databases in the digital era, content-based methods tailored for the speciﬁc media have to be available. In this paper we study the applicability of a mathematical framework for retrieving music in symbolic, polyphonic music databases in a content-based fashion. More speciﬁcally, we harness the mathematical morphology methodology for locating approximate occurrences of a given musical query pattern in a larger music database. To this end, we represent music symbolically using the well-known piano-roll representation (see Fig. 1(b)) and cast it into a twodimensional binary image. The representation used resembles that of a previously used technique based on point-pattern matching [14,11,12,10]; the applied methods themselves, however, are very diﬀerent. The advantage of using our novel approach is that it enables more ﬂexible matching for polyphonic music, allowing local jittering on both time and pitch values of the notes. This has been problematic to achieve with the polyphonic methods based on the point-pattern S. Ystad et al. (Eds.): CMMR 2010, LNCS 6684, pp. 321–337, 2011. c Springer-Verlag Berlin Heidelberg 2011

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Fig. 1. (a) The ﬁrst two measures of Bach’s Invention 1. (b) The same polyphonic melody cast into a 2-D binary image. (c) A query pattern image with one extra note and various time and pitch displacements. (d) The resulting image after a blur rank order ﬁltering operation, showing us the potential matches.

matching. Moreover, our approach provides the user with an intuitive, visual way of deﬁning the allowed approximations for a query in hand. In [8], Karvonen and Lemstr¨om suggested the use of this framework for music retrieval purposes. We extend and complement their ideas, introduce and implement new algorithms, and carry out experiments to show their eﬃciency and eﬀectiveness. The motivation to use symbolic methods is twofold. Firstly, there is a multitude of symbolic music databases where audio methods are naturally not of use. In addition, the symbolic methods allow for distributed matching, i.e., occurrences of a query pattern are allowed to be distributed across the instruments (voices) or to be hidden in some other way in the matching fragments of the polyphonic database. The corresponding symbolic and audio ﬁles may be aligned by using mapping tools [7] in order to be able to play back the matching part in an audio form. 1.1

Representation and Problem Specifications

In this paper we deal with symbolically encoded, polyphonic music for which we use the pointset representation (the pitch-against-time representation of noteon information), as suggested in [15], or the extended version of the former, the horizontal-line-segment representation [14], where note durations are also explicitly given. The latter representation is equivalent to the well-known piano-roll representation (see e.g. Fig. 1(b)), while the former omits the duration information of the line segments and uses only the onset information of the notes (the starting points of the horizontal line segments). As opposed to the algorithms based on point-pattern matching where the piano-roll representation is a mere visualization of the underlying representation, here the visualization IS the representation: the algorithms to be given operate on binary images of the onset points or the horizontal line segments that correspond to the notes of the given query pattern and the database. Let us denote by P the pattern to be searched for in a database, denoted by T . We will consider the three problems, P1-P3, speciﬁed in [14], and their

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generalizations, AP1-AP3, to approximative matching where local jittering is allowed. The problems are as follows: – (Approximative) complete subset matching: Given P and T in the pointset representation, ﬁnd translations of P such that all its points match (P1) / match approximatively (AP1) with some points in T . – (Approximative) maximal partial subset matching: Given P and T in the pointset representation, ﬁnd all translations of P that give a maximal (P2) / an approximative and maximal (AP2) partial match with points in T. – (Approximative) longest common shared time matching: Given P and T in the horizontal line segment representation, ﬁnd translations of P that give the longest common (P3) / the approximative longest common (AP3) shared time with T , i.e., the longest total length of the (approximatively) intersected line segments of T and those of translated P . Above we have deliberately been vague in the meaning of an approximative match: the applied techniques enable the user to steer the approximation in the desired direction by means of shaping the structuring element used, as will be shown later in this paper. Naturally, an algorithm capable of solving problem AP1, AP2 or AP3 would also be able to solve the related, original nonapproximative problem P1, P2 or P3, respectively, because an exact match can be identiﬁed with zero approximation.

2 2.1

Background Related Work

Let us denote by P + f a translation of P by vector f , i.e., vector f is added to each m component of P separately: P + f = p1 + f, p2 + f, . . . , pm + f . Problem AP1 can then be expressed as the search for a subset I of T such that P + f I for some f and some similarity relation ; in the original P1 setting the relation is to be replaced by the equality relation =. It is noteworthy that the mathematical translation operation corresponds to two musically distinct phenomena: a vertical move corresponds to transposition while a horizontal move corresponds to aligning the pattern and the database time-wise. In [15], Wiggins et al. showed how to solve P1 and P2 in O(mn log(mn)) time. First, translations that map the maximal number of the m points of P to some points of T (of n points) are to be collected. Then the set of such translation vectors is to be sorted based on the lexicographic order, and ﬁnally the translation vector that is the most frequent is to be reported. If the reported vector f appears m times, it is also an occurrence for P1. With careful implementation of the sorting routine, the running time can be improved to O(mn log m) [14]. For P1, one can use a faster algorithm working in O(n) expected time and O(m) space [14]. In [5], Cliﬀord et al. showed that problem P2 is 3SUM-hard, which means that it is unlikely that one could ﬁnd an algorithm for the problem with a subquadratic

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running time. Interestingly enough, Minkowski addition and subtraction, which are the underlying basic operations used by our algorithms, are also known to be 3SUM-hard [1]. Cliﬀord et al. also gave an approximation algorithm for P2 working in time O(n log n). In order to be able to query large music databases in real time, several indexing schemes have been suggested. Clausen et al. used an inverted ﬁle index for a P2-related problem [4] that achieves sublinear query times in the length of the database. In their approach, eﬃciency is achieved at the cost of robustness: the information extraction of their method makes the approach non-applicable to problems P1 and P2 as exact solutions. Another very general indexing approach was recently proposed in [13]: Typke et al.’s use of a metric index has the advantage that it works under robust geometric similarity measures. However, it is diﬃcult to adapt it to support translations and partial matching. More recently, Lemstr¨om et al. [10] introduced an approach that combines indexing and ﬁltering achieving output sensitive running times for P1 and P2: O(sm) and O(sm log m), respectively, where s is the number of candidates, given by a ﬁlter, that are to be checked. Typically their algorithms perform 1-3 orders of magnitude faster than the original algorithms by Ukkonen et al. [14]. Romming and Selfridge-Field [12] introduced an algorithm based on geometric hashing. Their solution that combines the capability of dealing with polyphonic music, transposition invariance and time-scale invariance, works in O(n3 ) space and O(n2 m3 ) time, but by applying windowing on the database, the complexities can be restated as O(w2 n) and O(wnm3 ), respectively, where w is the maximum number of events that occur in any window. Most recently, Lemstr¨ om [9] generalized Ukkonen et al.’s P1 and P2 algorithms [14] to be time-scale invariant. With windowing the algorithms work in O(mΣ log Σ) time and O(mΣ) space, where Σ = O(wn) when searching for exact occurrences and Σ = O(nw2 ) when searching for partial occurrences; without windowing the respective complexities are O(ρn2 log n) and O(ρn2 ); ρ = O(m) for the exact case, ρ = O(m2 ) for the partial case. With all the above algorithms, however, their applicability to real-world problems is reduced due to the fact that, beyond the considered invariances, matches have to be mathematically exact, and thus, for instance, performance expression and error is diﬃcult to account for. We bridge this gap by introducing new algorithms based on the mathematical morphology framework where allowed error tolerance can be elegantly embedded in a query. 2.2

Mathematical Morphology

Mathematical morphology (MM) is a theoretically well-deﬁned framework and the foundation of morphological image processing. Originally developed for binary images in the 1960s, it was subsequently extended to grey-scale images and ﬁnally generalized to complete lattices. MM is used for quantitative analysis and processing of the shape and form of spatial structures in images. It ﬁnds many applications in computer vision, template matching and pattern recognition problems. Morphological image processing is used for pre- and post-processing of

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images in a very similar way to conventional image ﬁlters. However, the focus in MM-based methods is often in extracting attributes and geometrically meaningful data from images, as opposite to generating ﬁltered versions of images. In MM, sets are used to represent objects in an image. In binary images, the sets are members of the 2-D integer space Z2 . The two fundamental morphological operations, dilation and erosion, are non-linear neighbourhood operations on two sets. They are based on the Minkowski addition and subtraction [6]. Out of the two sets, the typically smaller one is called the structuring element (SE). Dilation performs a maximum on the SE, which has a growing eﬀect on the target set, while erosion performs a minimum on the SE and causes the target set to shrink. Dilation can be used to ﬁll gaps in an image, for instance, connecting the breaks in letters in a badly scanned image of a book page. Erosion can be used, for example, for removing salt-and-pepper type noise. One way to deﬁne dilation is ˆ + f ) ∩ A = ∅}, A ⊕ B = {f ∈ Z2 | (B (1) ˆ its reﬂection (or rotation by where A is the target image, B is the SE, and B 180 degrees). Accordingly, erosion can be written A B = {f ∈ Z2 | (B + f ) ⊆ A}.

(2)

Erosion itself can be used for pattern matching. Foreground pixels in the resulting image mark the locations of the matches. Any shape, however, can be found in an image ﬁlled with foreground. If the background also needs to match, erosion has to be used separately also for the negations of the image and the structuring element. Intersecting these two erosions leads to the desired result. This procedure is commonly known as the hit-or-miss transform or hit-miss transform (HMT): HMT(A, B) = (A B) ∩ (AC B C ).

(3)

HMT is guaranteed to give us a match only if our SE perfectly matches some object(s) in the image. The requirement for a perfect match is that the background must also match (i.e., it cannot contain additional pixels) and that each object has at least a one-pixel-thick background around it, separating it from other objects (in this case B C actually becomes W − B, where W is a window of ”on” pixels slightly larger than B). In cases where we are interested in partially detecting patterns within a set, we can ignore the background and reduce HMT to simple erosion. This is clearly the case when we represent polyphonic music as binary 2-D images. We use this simpliﬁed pattern detection scheme in one of the algorithms developed in Chapter 3.

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In [8] Karvonen and Lemstr¨om introduced four algorithms based on the mathematical morphology framework and gave their MATLAB implementations. Our

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closer examination revealed common principles behind the four algorithms; three of them were virtually identical to each other. The principles on which our two algortihms to be introduced rely are explained by Bloomberg and Maragos [2]. Having HMT as the main means of generalizing erosion, they present three more, which can be combined in various ways. They also name a few of the combinations. Although we can ﬁnd some use for HMT, its beneﬁt is not signiﬁcant in our case. But two of the other tricks proved to be handy, and particularly their combination, which is not mentioned by Bloomberg and Maragos. We start with erosion as the basic pattern matching operation. The problem with erosion is its lack of ﬂexibility: every note must match and no jittering is tolerated. Performing the plain erosion solves problem P1. We present two ways to gain ﬂexibility. – Allow partial matches. This is achieved by moving from P1 to P2. – Handle jittering. This is achieved by moving from P1 to AP1. Out of the pointset problems, only AP2 now remains unconsidered. It can be solved, however, by combining the two tricks above. We will next explain how these improvements can be implemented. First, we concentrate on the pointset representation, then we will deal with line segments. 3.1

Allowing Partial Matches

For a match to be found with plain erosion, having applied a translation, the whole foreground area of the query needs to be covered by the database foreground. In pointset representation this means that there needs to be a corresponding database note for each note in the query. To allow missing notes, a coverage of only some speciﬁed portion of the query foreground suﬃces. This is achieved by replacing erosion by a more general ﬁlter. For such generalization, Bloomberg and Maragos propose a binary rank order filter (ROF) and threshold convolution (TC). In addition to them, one of the algorithms in [8] was based on correlation. These three methods are connected to each other, as discussed next. For every possible translation f , the binary rank order ﬁlter counts the ratio |(P + f ) ∩ T | , |P | where |P | is the number of foreground pixels in the query. If the ratio is greater than or equal to a speciﬁed threshold value, it leaves a mark in the resulting image, representing a match. This ratio can be seen as a conﬁdence score (i.e., a probability) that the query foreground occurs in the database foreground at some point. It is noteworthy that plain erosion is a special case of binary ROF, where the threshold ratio is set to 1. By lowering the threshold we impose looser conditions than plain erosion on detecting the query. Correlation and convolution operate on greyscale images. Although we deal with binary images, these operations are useful because ROF can be implemented

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using correlation and thresholding. When using convolution as a pattern matching operation, it can be seen as a way to implement correlation: rotating the query pattern 180 degrees and then performing convolution on real-valued data has almost the same eﬀect as performing correlation, the only diﬀerence being that the resulting marks appear in the top-left corner instead of the bottomright corner of the match region. Both can be eﬀectively implemented using the Fast Fourier Transform (FFT). Because of this relation between correlation and convolution, ROF and TC are actually theoretically equivalent. When solving P2, one may want to search for maximal partial matches (instead of threshold matches). This is straightforwardly achieved by implementing ROF by using correlation. Although our ROF implementation is based on correlation, we will call the method ROF since it oﬀers all the needed functionality. 3.2

Tolerating Jittering

Let us next explain the main asset of our algorithms as compared to the previous algorithms. In order to tolerate jittering, the algorithms should be able to ﬁnd corresponding database elements not only in the exact positions of the translated query elements, but also in their near proximity. Bloomberg and Vincent [3] introduced a technique for adding such toleration into HMT. They call it blur hit-miss transform (BHMT). The trick is to dilate the database images (both the original and the complement) by a smaller, disc-shaped structuring element before the erosions are performed. This can be written BHMT(A, B1 , B2 , R1 , R2 ) = [(A ⊕ R1 ) B1 ] ∩ [(AC ⊕ R2 ) B2 ],

(4)

where A is the database and AC its complement, B1 and B2 are the query foreground and background and R1 and R2 are the blur SEs. The technique is also eligible for the plain erosion. We choose this method for jitter toleration and call it blur erosion: A b (B, R) = (A ⊕ R) B.

(5)

The shape of the preprocessive dilation SE does not have to be a disc. In our case, where the dimensions under consideration are time and pitch, a natural setting comprises user-speciﬁed thresholds for the dimensions. This leads us to rectangular SEs with eﬃcient implementations. In practice, dilation operations are useful in the time dimension, but applying it in pitch dimension often results in false (positive) matches. Instead, a blur of just one semitone is very useful because the queries often contain pitch quantization errors. 3.3

Combining the Two

By applying ROF we allow missing notes, thus being able to solve problem P2. The jitter toleration is achieved by using blurring, thus solving AP1. In order to

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be able to solve AP2, we combine these two. In order to correctly solve AP2, the dilation has to be applied to the database image. With blurred ROF a speed-up can be obtained (with the cost of false positive matches) by dilating the query pattern instead of the database image. If there is no need to adjust the dilation SE, the blur can be applied to the database in a preprocessing phase. Note also that if both the query and the database were dilated, it would grow the distance between the query elements and the corresponding database elements which, subsequently, would gradually decrease the overlapping area. Figure 2 illustrates the relations between the discussed methods. Our interest is on blur erosion and blur ROF (underlined in the Figure), because they can be used to solve the approximate problems AP1 and AP2. 3.4

Line Segment Representation

Blur ROF is applicable also for solving AP3. In this case, however, the blur is not as essential: in a case of an approximate occurrence, even if there was some jittering in the time dimension, a crucial portion of the line segments would typically still overlap. Indeed, ROF without any blur solves exactly problem P3. By using blur erosion without ROF on line segment data, we get an algorithm that does not have an existing counterpart. Plain erosion is like P3 with the extra requirement of full matches only, the blur then adds error toleration to the process.

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Thus far we have not been interested in what happens in the background of an occurrence; we have just searched for occurrences of the query pattern that are intermingled in the polyphonic texture of the database. If, however, no extra notes were allowed in the time span of an occurrence, we would have to consider the background as well. This is where we need the hit-miss transform. Naturally, HMT is applicable also in decreasing the number of false positives in cases where the query is assumed to be comprehensive. With HMT as the third way of generalizing erosion, we complement the classiﬁcation in Figure 2. Combining HMT with the blur operation, we have to slightly modify the original BHMT to meet the special requirements of the domain. In the original form, when matching the foreground, tiny background dots are ignored because they are considered to be noise. In our case, with notes represented as single pixels or thin line segments, all the events would be ignored during the background matching; the background would always match. To achieve the desired eﬀect, instead of dilating the complemented database image, we need to erode the complemented query image by the same SE: BHMT*(A, B1 , B2 , R1 , R2 ) = [(A ⊕ R1 ) B1 ] ∩ [AC (B2 R2 )],

(6)

where A is the database and AC its complement. B1 and B2 are the query foreground and background, R1 and R2 are the blur SEs. If B2 is the complement of B1 , we can write B = B1 and use the form BHMT*(A, B, R1 , R2 ) = [(A ⊕ R1 ) B1 ] ∩ [AC (B ⊕ R2 )C ].

(7)

Another example where background matching would be needed is with line segment representation of long notes. In an extreme case, a tone cluster with a long duration forms a rectangle that can be matched with anything. Even long sounding chords can result in many false positives. This problem can be alleviated by using HMT with a tiny local background next to the ends of the line segments to separate the notes.

4

Experiments

The algorithms presented in this paper set new standards for ﬁnding approximative occurrences of a query pattern from a given database. There are no rivaling algorithms in this sense, so we are not able to fairly compare the performance of our algorithms to any existing algorithm. However, to give the reader a sense of the real-life performance of these approximative algorithms, we compare the running times of these to the existing, nonapproximative algorithms. Essentially this means that we are comparing the performance of the algorithms being able to solve AP1-AP3 to the ones that can solve only P1, P2 and P3 [14].

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In this paper we have sketched eight algorithms based on mathematical morphology. In our experiments we will focus on two of them: blur erosion and blur ROF, which can be applied to solve problems AP1-AP3. The special cases of these algorithms, where blur is not applied, are plain erosion and ROF. As our implementation of blur ROF is based on correlation, we will call it bluf correlation from now on. As there are no competitors that solve the problems AP1-3, we set our new algorithms against the original geometric algorithms named after the problem speciﬁcations P 1, P 2 and P 3 [14] to get an idea of their practical performance. For the performance of our algorithms, the implementation of dilation, erosion and correlation are crucial. For dilation and erosion, we rely on Bloomberg’s Leptonica library. Leptonica oﬀers an optimized implementation for rectangleshaped SEs, which we can utilize on the case of dilation. On the other hand, our erosion SEs tend to be much more complex and larger in size (we erode the databases with the whole query patterns). For correlation we use Fast Fourier Transform implemented in the FFTW library. This operation is quite heavy calculation-wise compared to erosion, since it has to operate with ﬂoating point complex numbers. The performance of the reference algorithms, used to solve the original, nonapproximative problems, depend mostly on the number of notes in the database and in the query. We experiment on how the algorithms scale up as a function of the database and query pattern lengths. It is also noteworthy that the note density can make a signiﬁcant diﬀerence in the performance, as the time consumption of our algorithms mostly grow along with the size of the corresponding images. The database we used in our experiments consists of MIDI ﬁles from the Mutopia collection 1 that contains over 1.4 million notes. These ﬁles were converted to various other formats required by the algorithms, such as binary images of 1

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pointset and line segment types. On the experiments of the eﬀects of varying pattern sizes, we selected randomly 16 pieces out of the whole database, each containing 16,000 notes. Five distinct queries were randomly chosen, and the median of their execution times was reported. When experimenting with varying database sizes, we chose a pattern size of 128 notes. The size of the images is also a major concern for the performance of our algorithms. We represent the pitch dimension as 128 pixels, since the MIDI pitch value range consists of 128 possible values. The time dimension, however, poses additional problems: it is not intuitively clear what would make a good time resolution. If we use too many pixel columns per second, the performances of our algorithms will be slowed down signiﬁcantly. On the ﬂip side of the coin, not using enough pixels per second would result in a loss of information as we would not be able to distinguish separate notes in rapid passages anymore. Before running the actual experiments, we decided to experiment on ﬁnding a suitable time resolution eﬃciency-wise. 4.1

Adjusting the Time Resolution

We tested the eﬀect of increasing time resolution on both blur erosion and blur correlation, and the results can be seen in Figure 3. With blur erosion, we can see a clear diﬀerence between the pointset representation and the line segment representation: in the line segment case, the running time of blur erosion seems to grow quadratically in relation to the growing time resolution, while in the pointset case, the growth rate seems to be clearly slower. This can be explained with the fact that the execution time of erosion depends on the size of the query foreground. In the pointset case, we still only mark the beginning point of the notes, so only SEs require extra space. In the line segment case, however, the growth is clearly linear.

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In the case of blur correlation, there seems to be nearly no eﬀect whether the input is in pointset or line segment form. The pointset and line segment curves in the ﬁgure of blur correlation collide, so we depicted only one of the curves in this case. Looking at the results, one can note that the time usage of blur erosion begins to grow quickly around 12 and 16 pixel columns per second. Considering that we do not want the performance of our algorithms to suﬀer too much, and the fact that we are deliberately getting rid of some nuances of information by blurring, we were encouraged to set the time resolution as low as 12 pixel columns per second. This time resolution was used in the further experiments. 4.2

Pointset Representation

Both P1 and P2 are problems, where we aim to ﬁnd an occurrence of a query pattern from a database, where both the database and the query are represented as pointsets. Our new algorithms add the support for approximation. Blur erosion solves problem AP1, ﬁnding exact approximative matches, whereas blur correlation ﬁnds also partial matches, thus solving AP2. We compared eﬃciency of the non-approximative algorithms to our new, approximative algorithms with varying query and database sizes. As an additional comparison point, we also included Cliﬀord et al.’s approximation algorithm [5], called the maximal subset matching (MSM) algorithm, in the comparison. MSM is based on FFT and its execution time does not depend on the query size. Analyzing the results seen in Figure 4, we note that the exact matching algorithm P 1 is the fastest algorithm in all settings. This was to be expected due to the linear behaviour of P 1 in the length of the database. P 1 also clearly outperforms its approximative counterpart, blur erosion. On the other hand, the 1e+06

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performance diﬀerence between the partial matching algorithms, blur correlation, MSM and P 2, is less radical. P 2 is clearly fastest of those with small query sizes, but as its time consumption grows with longer queries, it becomes the slowest with very large query sizes. Nevertheless, even with small query sizes, we believe that the enhanced error toleration is worth the extra time it requires. 4.3

Line Segment Representation

When experimenting with the line segment representations, we used P 3 as a reference algorithm for blur correlation. For blur erosion, we were not able to ﬁnd a suitable reference algorithm. Once again, however, blur erosion gives the reader some general sense of the eﬃciency of the algorithms working with line segment representation. The time consumption behaviour of P 3, blur erosion and blur correlation are depicted in Figure 5. The slight meandering seen in some of the curves is the result of an uneven distribution of notes in the database. Analyzing the graphs further, we notice that our blur correlation seems more competitive in this than in the pointset representation case. Again, we note that the independence of the length of the pattern makes blur correlation faster than P3 with larger pattern sizes: blur correlation outperforms P 3 once the pattern size exceeds 256 notes. Analyzing the results of experiments with diﬀering database sizes with a query pattern size of 128, the more restrictive blur erosion algorithm was fastest of the three. However, the three algorithms’ time consumptions were roughly of the same magnitude.

Fig. 6. (a) An excerpt of the database in a piano-roll representation with jittering window around each note. (b) Query pattern. (c) The query pattern inserted into the jittering windows of the excerpt of the database.

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Our experiments also conﬁrmed our claim of blur correlation handling jittering better than P 3. We were expecting this, and Figure 6 illustrates an idiomatic case where P 3 will not ﬁnd a match, but blur correlation will. In this case we have a query pattern that is an excerpt of the database, with the distinction that some of the notes have been tilted either time-wise or pitch-wise. Additionally one note has been split into two. Blur correlation ﬁnds a perfect match in this case, whereas P 3 cannot, unless the threshold for the total common length is exceeded. We were expecting this kind of results, since intuitively P 3 cannot handle this kind of jittering as well as morphological algorithms do. 4.4

Finding Fugue Theme Entries

To further demonstrate the assets of the blur technique, we compared P 1 and blur erosion in the task of ﬁnding the theme entries in J. S. Bach’s Fugue no. 16 in G minor, BWV861, from the Well-Tempered-Clavier, Book 1. The imitations of a fugue theme often have slight variation. The theme in our case is shown in Figure 7. In the following imitation, there is a little diﬀerence. The ﬁrst interval is a minor third instead of a minor second. This prevents P 1 ﬁnding a match here. But with a vertical dilation of two pixels blur erosion managed to ﬁnd the match. Figure 8 shows three entries of the theme. The ﬁrst one has some variation at the end and was only found by blur erosion. The second is an exact match. Finally, the last entry could not be found by either of the algorithms, because it has too much variation. In total, blur erosion found 16 occurrences, while P 1 found only six. If all the entries had diﬀered only in one or two notes, it would have been easy to ﬁnd them using P 2. For some of the imitations, however, less than

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half of the notes match exactly the original form of the theme (see Figure 9). Nevertheless, these imitations are fairly easily recognized visually and audibly. Our blur-erosion algorithm found them all.

5

Conclusions

In this paper, we have combined existing image processing methods based on mathematical morphology to construct a collection of new pattern matching algorithms for symbolic music represented as binary images. Our aim was to gain an improved error tolerance over the existing pointset-based and line-segmentbased algorithms introduced for related problems. Our algorithms solve three existing music retrieval problems, P1, P2 and P3. Our basic algorithm based on erosion solves the exact matching problem P1. To successfully solve the other two, we needed to relax the requirement of exact matches, which we did by applying a rank order ﬁltering technique. Using this relaxation technique, we can solve both the partial pointset matching problem P2, and also the line segment matching problem P3. By introducing blurring in the form of preprocessive dilation, the error tolerance of these morphological algorithms can be improved. That way the algorithms are able to tolerate jittering in both the time and the pitch dimension. Comparing to the solutions of the non-approximative problems, our new algorithms tend to be somewhat slower. However, they are still comparable performance-wise, and actually even faster in some cases. As the most important novelty of our algorithms is the added error tolerance given by blurring, we

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think that the slowdown is rather restrained compared to the added usability of the algorithms. We expect our error-tolerant methods to give better results in real-world applications when compared to the rivaling algorithms. As future work, we plan on researching and setting up a relevant ground truth, as without a ground truth, we cannot adequately measure the precision and recall on the algorithms. Other future work could include investigating the use of greyscale morphology for introducing more ﬁne-grained control over approximation.

Acknowledgements This work was partially supported by the Academy of Finland, grants #108547, #118653, #129909 and #218156.

References 1. Barrera Hern´ andez, A.: Finding an o(n2 log n) algorithm is sometimes hard. In: Proceedings of the 8th Canadian Conference on Computational Geometry, pp. 289–294. Carleton University Press, Ottawa (1996) 2. Bloomberg, D., Maragos, P.: Generalized hit-miss operators with applications to document image analysis. In: SPIE Conference on Image Algebra and Morphological Image Processing, pp. 116–128 (1990) 3. Bloomberg, D., Vincent, L.: Pattern matching using the blur hit-miss transform. Journal of Electronic Imaging 9(2), 140–150 (2000) 4. Clausen, M., Engelbrecht, R., Meyer, D., Schmitz, J.: Proms: A web-based tool for searching in polyphonic music. In: Proceedings of the International Symposium on Music Information Retrieval (ISMIR 2000), Plymouth, MA (October 2000) 5. Cliﬀord, R., Christodoulakis, M., Crawford, T., Meredith, D., Wiggins, G.: A fast, randomised, maximal subset matching algorithm for document-level music retrieval. In: Proceedings of the 7th International Conference on Music Information Retrieval (ISMIR 2006), Victoria, BC, Canada, pp. 150–155 (2006) 6. Heijmans, H.: Mathematical morphology: A modern approach in image processing based on algebra and geometry. SIAM Review 37(1), 1–36 (1995) 7. Hu, N., Dannenberg, R., Tzanetakis, G.: Polyphonic audio matching and alignment for music retrieval. In: Proc. IEEE WASPAA, pp. 185–188 (2003) 8. Karvonen, M., Lemstr¨ om, K.: Using mathematical morphology for geometric music information retrieval. In: International Workshop on Machine Learning and Music (MML 2008), Helsinki, Finland (2008) 9. Lemstr¨ om, K.: Towards more robust geometric content-based music retrieval. In: Proceedings of the 11th International Society for Music Information Retrieval Conference (ISMIR 2010), Utrecht, pp. 577–582 (2010) 10. Lemstr¨ om, K., Mikkil¨ a, N., M¨ akinen, V.: Filtering methods for content-based retrieval on indexed symbolic music databases. Journal of Information Retrieval 13(1), 1–21 (2010) 11. Lubiw, A., Tanur, L.: Pattern matching in polyphonic music as a weighted geometric translation problem. In: Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), Barcelona, pp. 289–296 (2004)

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12. Romming, C., Selfridge-Field, E.: Algorithms for polyphonic music retrieval: The hausdorﬀ metric and geometric hashing. In: Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR 2007), Vienna, Austria (2007) 13. Typke, R.: Music Retrieval based on Melodic Similarity. Ph.D. thesis, Utrecht University, Netherlands (2007) 14. Ukkonen, E., Lemstr¨ om, K., M¨ akinen, V.: Geometric algorithms for transposition invariant content-based music retrieval. In: Proceedings of the 4th International Conference on Music Information Retrieval (ISMIR 2003), Baltimore, MA, pp. 193–199 (2003) 15. Wiggins, G.A., Lemstr¨ om, K., Meredith, D.: SIA(M)ESE: An algorithm for transposition invariant, polyphonic content-based music retrieval. In: Proceedings of the 3rd International Conference on Music Information Retrieval (ISMIR 2002), Paris, France, pp. 283–284 (2002)

Melodic Similarity through Shape Similarity Julián Urbano, Juan Lloréns, Jorge Morato, and Sonia Sánchez-Cuadrado University Carlos III of Madrid Department of Computer Science Avda. Universidad, 30 28911 Leganés, Madrid, Spain {jurbano,llorens}@inf.uc3m.es, {jorge,ssanchec}@ie.inf.uc3m.es

Abstract. We present a new geometric model to compute the melodic similarity of symbolic musical pieces. Melodies are represented as splines in the pitchtime plane, and their similarity is computed as the similarity of their shape. The model is very intuitive and it is transposition and time scale invariant. We have implemented it with a local alignment algorithm over sequences of n-grams that define spline spans. An evaluation with the MIREX 2005 collections shows that the model performs very well, obtaining the best effectiveness scores ever reported for these collections. Three systems based on this new model were evaluated in MIREX 2010, and the three systems obtained the best results. Keywords: Music information retrieval, melodic similarity, interpolation.

1 Introduction The problem of Symbolic Melodic Similarity, where musical pieces similar to a query should be retrieved, has been approached from very different points of view [24][6]. Some techniques are based on string representations of music and editing distance algorithms to measure the similarity between two pieces[17]. Later work has extended this approach with other dynamic programming algorithms to compute global- or local-alignments between the two musical pieces [19][11][12]. Other methods rely on music representations based on n-grams [25][8][2], and other methods represent music pieces as geometric objects, using different techniques to calculate the melodic similarity based on the geometric similarity of the two objects. Some of these geometric methods represent music pieces as sets of points in the pitch-time plane, and then compute geometric similarities between these sets [26][23][7]. Others represent music pieces as orthogonal polynomial chains crossing the set of pitch-time points, and then measure the similarity as the minimum area between the two chains [30][1][15]. In this paper we present a new model to compare melodic pieces. We adapted the local alignment approach to work with n-grams instead of with single notes, and the corresponding substitution score function between n-grams was also adapted to take into consideration a new geometric representation of musical sequences. In this geometric representation, we model music pieces as curves in the pitch-time plane, and compare them in terms of their shape similarity. S. Ystad et al. (Eds.): CMMR 2010, LNCS 6684, pp. 338–355, 2011. © Springer-Verlag Berlin Heidelberg 2011

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In the next section we outline several problems that a symbolic music retrieval system should address, and then we discuss the general solutions given in the literature to these requirements. Next, we introduce our geometric representation model, which compares two musical pieces by their shape, and see how this model addresses the requirements discussed. In section 5 we describe how we have implemented our model, and in section 6 we evaluate it with the training and evaluation test collections used in the MIREX 2005 Symbolic Melodic Similarity task (for short, we will refer to these collections as Train05 and Eval05) [10][21][28]. Finally, we finish with conclusions and lines for further research. An appendix reports more evaluation results at the end.

2 Melodic Similarity Requirements Due to the nature of the information treated in Symbolic Melodic Similarity[18], there are some requirements that have to be considered from the very beginning when devising a retrieval system. Byrd and Crawford identified some requirements that they consider every MIR system should meet, such as the need of cross-voice matching, polyphonic queries or the clear necessity of taking into account both the horizontal and vertical dimensions of music[5].Selfridge-Field identified three elements that may confound both the users when they specify the query and the actual retrieval systems at the time of computing the similarity between two music pieces: rests, repeated notes and grace notes [18]. In terms of cross-voice and polyphonic material, she found five types of melody considered difficult to handle: compound, self-accompanying, submerged, roving and distributed melodies. Mongeau and Sankoff addressed repeated notes and refer to these situations as fragmentation and consolidation [17]. We list here some more general requirements that should be common to any Symbolic Melodic Similarity system, as we consider them basic for the general user needs. These requirements are divided in two categories: vertical (i.e. pitch) and horizontal (i.e. time). 2.1 Vertical Requirements Vertical requirements regard the pitch dimension of music: octave equivalence, degree equality, note equality, pitch variation, harmonic similarity and voice separation. A retrieval model that meets the first three requirements is usually regarded as transposition invariant. 2.1.1 Octave Equivalence When two pieces differ only in the octave they are written in, they should be considered the same one in terms of melodic similarity. Such a case is shown in Fig. 1, with simple versions of the main riff in Layla, by Dereck and the Dominos. It has been pointed out that faculty or music students may want to retrieve pieces within some certain pitch range such as C5 up to F#3, or every work above A5 [13].However, this type of information need should be easily handled with

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Fig. 1. Octave equivalence

metadata or a simple traverrse through the sequence. We argue that users without such a strong musical backgroun nd will be interested in the recognition of a certain piitch contour, and such cases are a much more troublesome because some measuree of melodic similarity has to be calculated. This is the case of query by humm ming applications. 2.1.2 Degree Equality The score at the top of Fig g. 1 shows a melody in the F major tonality, as well as the corresponding pitch and to onality-degree for each note. Below, Fig. 2 shows exacctly the same melody shifted 7 semitones s downwards to the Bb major tonality.

Fig. 2. Degree equality

The tonality-degrees useed in both cases are the same, but the resultant notes are not. Nonetheless, one would consider the second melody a version of the first oone, me in terms of pitch contour. Therefore, they should be because they are the sam considered the same one by b a retrieval system, which should also consider possiible modulations where the key changes somewhere throughout the song. 2.1.3 Note Equality We could also consider thee case where exactly the same melodies, with exactly the same notes, are written in different d tonalities and, therefore, each note corresponds to a different tonality-degree in each case. Fig. 3 shows such a case, with the saame t C major tonality. melody as in Fig. 1, but in the Although the degrees do o not correspond one to each other, the actual notes do, so both pieces should be consiidered the same one in terms of melodic similarity.

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Fig. 3. Note equality

2.1.4 Pitch Variation Sometimes, a melody is alttered by changing only the pitch of a few particular nootes. For instance, the first melo ody in Fig. 1 might be changed by shifting the 12th nnote from D7 to A6 (which actu ually happens in the original song). Such a change shoould not make a retrieval system m disregard that result, but simply rank it lower, after the exactly-equal ones. Thus, the retrieval process should not consider only exxact y is part of a piece in the repository (or the other w way matching, where the query around). Approximate mattching, where documents can be considered similar tto a query to some degree, sho ould be the way to go. This is of particular interest for scenarios like query by hu umming, where it is expected to have slight variationss in pitch in the melody hummeed by the user. 2.1.5 Harmonic Similaritty Another desired feature wo ould be to match harmonic pieces, both with harmonic and melodic counterparts. For instance, i in a triad chord (made up by the root note andd its major third and perfect fifth h intervals), one might recognize only two notes (typicaally the root and the perfect fiftth). However, some other might recognize the root and the major third, or just the roott, or even consider them as part of a 4-note chord such aas a major seventh chord (whicch adds a major seventh interval). Fig. 4 shows the saame piece as in the top of Fig. 1, but with some intervals added to make the song m more harmonic. These two piecess have basically the same pitch progression, but with soome ornamentation, and they sho ould be regarded as very similar by a retrieval system.

Fig. 4. Harmonic similarity

Thus, a system should d be able to compare harmony wholly and partiaally, considering again the Pitcch Variation problem as a basis to establish differennces between songs. 2.1.6 Voice Separation Fig. 5 below depicts a piano o piece with 3 voices, which work together as a whole, but could also be treated individ dually.

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Fig. 5. Voice separation

Indeed, if this piece werre played with a flute only one voice could be perform med, even if some streaming efffect were produced by changing tempo and timbre for ttwo voices to be perceived by a listener [16]. Therefore, a query containing only one vooice should match with this piecce in case that voice is similar enough to any of the thhree marked in the figure. ments 2.2 Horizontal Requirem Horizontal requirements regard r the time dimension of music: time signatture equivalence, tempo equivallence, duration equality and duration variation. A retrieeval model that meets the secon nd and third requirements is usually regarded as time sccale invariant. 2.2.1 Time Signature Equ uivalence The top of Fig. 6 depicts a simplified version of the beginning of op. 81 no. 10 byy S. 4 time signature. If a 4/4 time signature were used, likee in Heller, with its original 2/4 the bottom of Fig. 6, the pieece would be split into bars of duration 4 crotchets each..

Fig. 6. Time signature equivalence

The only difference betw ween these two pieces is actually how intense some nootes should be played. Howeverr, they are in essence the same piece, and no regular listeener would tell the difference. Therefore, we believe the time signature should nott be g musical performances in terms of melodic similarity. considered when comparing 2.2.2 Tempo Equivalencee For most people, the piecee at the top of Fig. 6, with a tempo of 112 crotchets per minute, would sound like th he one in Fig. 7, where notes have twice the length but the

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Fig. 7. Tempo equivalence

whole score is played twicce as fast, at 224 crotchets per minute. This two channges result in exactly the same acctual time. On the other hand, it might also be considered a tempo of 56 crotchets per minnute and notes with half the durration. Moreover, the tempo can change somewhere in the middle of the melody, and therefore change the actual time of each note afterwarrds. gths cannot be considered as the only horizontal measuure, Therefore, actual note leng because these three pieces would w sound the same to any listener. 2.2.3 Duration Equality mpo If the melody at the top of Fig. 6 were played slower or quicker by means of a tem variation, but maintaining the rhythm, an example of the result would be like the score in Fig. 8.

Fig. 8. Duration equality

Even though the melodiic perception does actually change, the rhythm does nnot, and neither does the pitch contour. Therefore, they should be considered as virtuaally the same, maybe with somee degree of dissimilarity based on the tempo variation. 2.2.4 Duration Variation n As with the Pitch Variation n problem, sometimes a melody is altered by changing oonly the rhythm of a few notes. For F instance, the melody in Fig. 9 maintains the same piitch contour as in Fig. 6, but chaanges the duration of some notes.

Fig. 9. Duration variation

Variations like these arre common and they should be considered as well, jjust like the Pitch Variation pro oblem, allowing approximate matches instead of just exxact ones.

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3 General Solutions to the Requirements Most of these problems have been already addressed in the literature. Next, we describe and evaluate the most used and accepted solutions. 3.1 Vertical Requirements The immediate solution to the Octave Equivalence problem is to consider octave numbers with their relative variation within the piece. Surely, a progression from G5 to C6 is not the same as a progression from G5 to C5. For the Degree Equality problem it seems to be clear that tonality degrees must be used, rather than actual pitch values, in order to compare two melodies. However, the Note Equality problem suggests the opposite. The accepted solution for these three vertical problems seems to be the use of relative pitch differences as the units for the comparison, instead of the actual pitch or degree values. Some approaches consider pitch intervals between two successive notes [11][8][15], between each note and the tonic (assuming the key is known and failing to meet the Note Equality problem) [17], or a mixture of both [11]. Others compute similarities without pitch intervals, but allowing vertical translations in the time dimension [1][19][30]. The Voice Separation problem is usually assumed to be solved in a previous stage, as the input to these systems uses to be a single melodic sequence. There are approximations to solve this problem [25][14]. 3.2 Horizontal Requirements Although the time signature of a performance is worth for other purposes such as pattern search or score alignment, it seems to us that it should not be considered at all when comparing two pieces melodically. According to the Tempo Equivalence problem, actual time should be considered rather than score time, since it would be probably easier for a regular user to provide actual rhythm information. On the other hand, the Duration Equality problem requires the score time to be used instead. Thus, it seems that both measures have to be taken into account. The actual time is valuable for most users without a musical background, while the score time might be more valuable for people who do have it. However, when facing the Duration Variation problem it seems necessary to use some sort of timeless model. The solution could be to compare both actual and score time [11], or to use relative differences between notes, in this case with the ratio between two notes’ durations [8]. Other approaches use a rhythmical framework to represent note durations as multiples of a base score duration [2][19][23], which does not meet the Tempo Equivalence problem and hence is not time scale invariant.

4 A Model Based on Interpolation We developed a new geometric model that represents musical pieces with curves in the pitch-time plane, extending the model with orthogonal polynomial chains [30][1][15]. Notes are represented as points in the pitch-time plane, with positions

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relative to their pitch and duration d differences. Then, we define the curve C(t) as the interpolating curve passing g through each point (see Fig. 10). Should the song hhave multiple voices, each one would w be placed in a different pitch-time plane, sharing the same time dimension, but with w a different curve Ci(t) (where the subscript i indicaates the voice number). Note thaat we thus assume the voices are already separated. With this representation, the similarity between two songs could be thought off as ween the two curves they define. Every vertical requirem ment the similarity in shape betw identified in section 2.1wou uld be met with this representation: a song with an octtave shift would keep the same shape; s if the tonality changed the shape of the curve woould not be affected either; and if the notes remained the same after a tonality change, so would the curve do. The Pitch Variation problem can be addressed analyticaally measuring the curvature diffference, and different voices can be compared individuaally in the same way because theey are in different planes.

Fig. 10. Mellody represented as a curve in a pitch-time plane

Same thing happens with h the horizontal requirements: the Tempo Equivalence and Duration Equality problem ms can be solved analytically, because they imply jusst a linear transformation in thee time dimension. For example, if the melody at the topp of Fig. 6 is defined with curve C(t) and the one in Fig. 7 is denoted with curve D(tt), it can be easily proved that C(2t)=D(t). Moreover, the Duration Variation probllem could be addressed analy ytically as the Pitch Variation problem, and the Tiime Signature Equivalence pro oblem is not an issue because the shape of the curvee is independent of the time sign nature. 4.1 Measuring Dissimilarrity with the Change in Shape Having musical pieces represented with curves, each one of them could be defiined f C(t)=antn+an-1tn-1+…+a1t+a0. The first derivativee of with a polynomial of the form this polynomial measures how h much the shape of the curve is changing at a particuular point in time (i.e. how the song changes). To measure the change of one curve w with respect to another, the area between the first derivatives could be used. h would mean just a shift in the a0 term. As it turns oout, Note that a shift in pitch when calculating the first derivative d of the curves this term is canceled, which is w why the vertical requirements arre met: shifts in pitch are not reflected in the shape of the curve, so they are not reflected r in the first derivative either. Therefore, this representation is transpositiion invariant. The song is actually defiined by the first derivative of its interpolating curve, C’(t). The dissimilarity between two t songs, say C(t) and D(t), would be defined as the aarea

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between their first derivatives (measured with the integral over the absolute value of their difference): diff(C, D) = |C'(t)-D'(t)|dt

(1)

The representation with orthogonal polynomial chains also led to the measurement of dissimilarity as the area between the curves [30][1]. However, such representation is not directly transposition invariant unless it used pitch intervals instead of absolute pitch values, and a more complex algorithm is needed to overcome this problem[15]. As orthogonal chains are not differentiable, this would be the indirect equivalent to calculating the first derivative as we do. This dissimilarity measurement based on the area between curves turns out to be a metric function, because it has the following properties: • Non-negativity, diff(C, D) ≥ 0: because the absolute value is never negative. • Identity of indiscernibles, diff(C, D) = 0 ⇔ C = D: because calculating the absolute value the only way to have no difference is with the same exact curve1. • Symmetry, diff(C, D) = diff(D, C): again, because the integral is over the absolute value of the difference. • Triangle inequality, diff(C, E) ≤ diff(C, D) + diff(D, E):

|C'(t) - D'(t)|dt +

|C'(t) - E'(t)|dt ≤

|C'(t) - D'(t)|dt +

|D'(t) - E'(t)|dt =

|C'(t) - D'(t)| +|D'(t) - E'(t)|dt

|C'(t) - D'(t)| +|D'(t) - E'(t)|dt ≥

|D'(t) - E'(t)|dt

|C'(t) - E'(t)| dt

Therefore, many indexing and retrieval techniques, like vantage objects[4], could be exploited if using this metric. 4.2 Interpolation with Splines The next issue to address is the interpolation method to use. The standard Lagrange interpolation method, though simple, is known to suffer the Runge’s Phenomenon [3]. As the number of points increases, the interpolating curve wiggles a lot, especially at the beginning and the end of the curve. As such, one curve would be very different from another one having just one more point at the end, the shape would be different and so the dissimilarity metric would result in a difference when the two curves are practically identical. Moreover, a very small difference in one of the points could translate into an extreme variation in the overall curve, which would make virtually impossible to handle the Pitch and Duration Variation problems properly (see top of Fig. 11). 1

Actually, this means that the first derivatives are the same, the actual curves could still be shifted. Nonetheless, this is the behavior we want.

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Fig. 11. Runge’s Phenomenon

A way around Runge's Phenomenon P is the use of splines (see bottom of Fig. 111). Besides, splines are also easy to calculate and they are defined as piece-w wise functions, which comes in handy when addressing the horizontal requirements. We ntal problems could be solved, as they implied just a linnear saw above that the horizon transformation of the form m D(t) ⇒ D(kt) in one of the curves. However, the calculation of the term k is anything but straightforward, and the transformattion would apply to the whole curve, c complicating the measurement of differences for the Duration Variation problem m. The solution would be to split the curve into spans, and define it as

Ci (t) =

ci,1 (t) ci,2 (t)

ti,1 ≤ t ≤ ti,kn ti,2 ≤ t ≤ ti,kn+1

ci,mi-kn +1 (t)

ti,mi-kn +1 ≤ t ≤ ti,mi

(2)

where ti,j denotes the onset time t of the j-th note in the i-th voice, mi is the length off the i-th voice, and kn is the spaan length. With this representation, linear transformatiions would be applied only to a single span without affecting the whole curve. Moreovver, ould be normalized from 0 to 1, making it easy to calcuulate the duration of the spans co the term k and comply with h the time scale invariance requirements. Most spline interpolatio on methods define the curve in parametric form (i.e. w with one function per dimension n). In this case, it results in one function for the pitch and one function for the time.. This means that the two musical dimensions couldd be compared separately, giviing more weight to one or the other. Therefore, the dissimilarity between two spans s c(t) and d(t) would be the sum of the pitch and tiime dissimilarities as measured by (1): ff(c, d) = kpdiffp(c, d) + ktdifft(c, d) diff

(3)

where diffp and difft are fu unctions as in (1) that consider only the pitch and tiime dimensions, respectively, and a kp and kt are fine tuning constants. Different woorks suggest that pitch is much more m important than time for comparing melodic similarrity, so more weight should be given g to kp [19][5][8][23][11].

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5 Implementation Geometric representations of music pieces are very intuitive, but they are not necessarily easy to implement. We could follow the approach of moving one curve towards the other looking for the minimum area between them [1][15]. However, this approach is very sensitive to small differences in the middle of a song, such as repeated notes: if a single note were added or removed from a melody, it would be impossible to fully match the original melody from that note to the end. Instead, we follow a dynamic programming approach to find an alignment between the two melodies [19]. Various approaches for melodic similarity have applied editing distance algorithms upon textual representations of musical sequences that assign one character to each interval or each n-gram [8]. This dissimilarity measure has been improved in recent years, and sequence alignment algorithms have proved to perform better than simple editing distance algorithms [11][12]. Next, we describe the representation and alignment method we use. 5.1 Melody Representation To practically apply our model, we followed a basic n-gram approach, where each ngram represents one span of the spline. The pitch of each note was represented as the relative difference to the pitch of the first note in the n-gram, and the duration was represented as the ratio to the duration of the whole n-gram. For example, an n-gram of length 4 with absolute pitches 〈74, 81, 72, 76〉 and absolute durations 〈240, 480, 240, 720〉, would be modeled as 〈81-74, 72-74, 76-74〉 = 〈7, -2, 2〉 in terms of pitch and 〈240, 480, 240, 720〉⁄1680 = 〈0.1429, 0.2857, 0.1429, 0.4286〉 in terms of duration. Note that the first note is omitted in the pitch representation as it is always 0. This representation is transposition invariant because a melody shifted in the pitch dimension maintains the same relative pitch intervals. It is also time scale invariant because the durations are expressed as their relative duration within the span, and so they remain the same in the face of tempo and actual or score duration changes. This is of particular interest for query by humming applications and unquantized pieces, as small variations in duration would have negligible effects on the ratios. We used Uniform B-Splines as interpolation method [3]. This results in a parametric polynomial function for each n-gram. In particular, an n-gram of length kn results in a polynomial of degree kn-1 for the pitch dimension and a polynomial of degree kn-1 for the time dimension. Because the actual representation uses the first derivatives, each polynomial is actually of degree kn-2. 5.2 Melody Alignment We used the Smith-Waterman local alignment algorithm [20], with the two sequences of overlapping spans as input, defined as in (2). Therefore, the input symbols to the alignment algorithm are actually the parametric pitch and time functions of a span,

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based on the above representation of n-grams. The edit operations we define for the Smith-Waterman algorithm are as follows: • • • •

Insertion: s(-, c).Adding a span c is penalized with the score–diff(c, ɸ(c)). Deletion: s(c, -). Deleting a span c is penalized with the score–diff(c, ɸ(c)). Substitution: s(c, d). Substituting a span c with d is penalized with –diff(c, d). Match: s(c, c). Matching a span c is rewarded with the score 2(kpμp+ktμt).

where ɸ(•) returns the null n-gram of • (i.e. an n-gram equal to • but with all pitch intervals set to 0), and μp and μt are the mean differences calculated by diffp and difft respectively over a random sample of 100,000 pairs of n-grams sampled from the set of incipits in the Train05 collection. We also normalized the dissimilarity scores returned by difft. From the results in Table 1 it can be seen that pitch dissimilarity scores are between 5 and 7 times larger than time dissimilarity scores. Therefore, the choice of kp and kt does not intuitively reflect the actual weight given to the pitch and time dimensions. For instance, the selection of kt=0.25, chosen in studies like [11], would result in an actual weight between 0.05 and 0.0357. To avoid this effect, we normalized every time dissimilarity score multiplying it by a factor λ = μp / μt. As such, the score of the match operation is actually defined as s(c, c) = 2μp(kp+kt), and the dissimilarity function defined in (3) is actually calculated as diff(c, d) = kp diffp(c, d) + λktdifft(c, d).

6 Experimental Results2 We evaluated the model proposed with the Train05 and Eval05 test collections used in the MIREX 2005 Symbolic Melodic Similarity Task [21][10], measuring the mean Average Dynamic Recall score across queries [22]. Both collections consist of about 580 incipits and 11 queries each, with their corresponding ground truths. Each ground truth is a list of all incipits similar to each query, according to a panel of experts, and with groups of incipits considered equally similar to the query. However, we have recently showed that these lists have inconsistencies whereby incipits judged as equally similar by the experts are not in the same similarity group and vice versa [28]. All these inconsistencies result in a very permissive evaluation where a system could return incipits not similar to the query and still be rewarded for it. Thus, results reported with these lists are actually overestimated, by as much as 12% in the case of the MIREX 2005 evaluation. We have proposed alternatives to arrange the similarity groups for each query, proving that the new arrangements are significantly more consistent than the original one, leading to a more robust evaluation. The most consistent ground truth lists were those called Any-1 [28]. Therefore, we will use these Any-1 ground truth lists from this point on to evaluate our model, as they offer more reliable results. Nonetheless, all results are reported in an appendix as if using the original ground truths employed in MIREX 2005, called All-2, for the sake of comparison with previous results.

2

All system outputs and ground truth lists used in this paper can be downloaded from http://julian-urbano.info/publications/

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To determine the value of the kn and kt parameters, we used a full factorial experimental design. We tested our model with n-gram lengths in the range kn∈{3, 4, 5, 6, 7}, which result in Uniform B-Spline polynomials of degrees 1 to 5. The value of kp was kept to 1, and kt was converted to nominal with levels kt∈{0, 0.1, 0.2, …, 1}. 6.1 Normalization Factor λ First, we calculated the mean dissimilarity scores μp and μt for each n-gram length kn, according to diffp and difft over a random sample of 100,000 pairs of n-grams. Table 1 lists the results. As mentioned, the pitch dissimilarity scores are between 5 and 7 times larger than the time dissimilarity scores, suggesting the use of the normalization factor λ defined above. Table 1. Mean and standard deviation of the diffp and difft functions applied upon a random sample of 100,000 pairs of n-grams of different sizes kn 3 4 5 6 7

μp 2.8082 2.5019 2.2901 2.1347 2.0223

σp 1.6406 1.6873 1.4568 1.4278 1.3303

μt 0.5653 0.494 0.4325 0.3799 0.2863

σt 0.6074 0.5417 0.458 0.3897 0.2908

λ = μp / μt 4.9676 5.0646 5.2950 5.6191 7.0636

There also appears to be a negative correlation between the n-gram length and the dissimilarity scores. This is caused by the degree of the polynomials defining the splines: high-degree polynomials fit the points more smoothly than low-degree ones. Polynomials of low degree tend to wiggle more, and so their derivatives are more pronounced and lead to larger areas between curves. 6.2 Evaluation with the Train05 Test Collection, Any-1 Ground Truth Lists The experimental design results in 55 trials for the 5 different levels of kn and the 11 different levels of kt. All these trials were performed with the Train05 test collection, ground truths aggregated with the Any-1 function [28]. Table 2 shows the results. In general, large n-grams tend to perform worse. This could probably be explained by the fact that large n-grams define the splines with smoother functions, and the differences in shape may be too small to discriminate musically perceptual differences. However, kn=3 seems to be the exception (see Fig. 12). This is probably caused by the extremely low degree of the derivative polynomials. N-grams of length kn=3 result in splines defined with polynomials of degree 2, which are then differentiated and result in polynomials of degree 1. That is, they are just straight lines, and so a small difference in shape can turn into a relatively large dissimilarity score when measuring the area. Overall, kn=4 and kn=5 seem to perform the best, although kn=4 is more stable across levels of kt. In fact, kn=4 and kt=0.6 obtain the best score, 0.7215. This result agrees with other studies where n-grams of length 4 and 5 were also found to perform

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Table 2. Mean ADR scores for each combination of kn and kt with the Train05 test collection, ground truth lists aggregated with the Any-1 function. kp is kept to 1. Bold face for largest scores per row and italics for largest scores per column. kt=0 0.6961 0.7046 0.7093 0.714 0.6823

kt=0.1 0.7067 0.7126 0.7125 0.7132 0.6867

kt=0.2 0.7107 0.7153 0.7191 0.7115 0.6806

kt=0.3 0.7106 0.7147 0.72 0.7088 0.6747

kt=0.4 0.7102 0.7133 0.7173 0.7008 0.6538

kt=0.5 0.7109 0.72 0.7108 0.693 0.6544

kt=0.6 0.7148 0.7215 0.704 0.6915 0.6529

kt=0.7 0.711 0.7202 0.6978 0.6874 0.6517

kt=0.8 0.7089 0.7128 0.6963 0.682 0.6484

kt=1 0.6962 0.709 0.6866 0.6763 0.6432

0.7 0.68 0.66 0.64

Mean ADR score

kt=0.9 0.7045 0.7136 0.6973 0.6765 0.6465

kn = 3 kn = 4 kn = 5 kn = 6 kn = 7

0.72

kn 3 4 5 6 7

0

0.1

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0.5

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0.8

0.9

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Fig. 12. Mean ADR scores for each combination of kn and kt with the Train05 test collection, ground truth lists aggregated with the Any-1 function. kp is kept to 1

better [8]. Moreover, this combination of parameters obtains a mean ADR score of 0.8039 when evaluated with the original All-2 ground truths (see Appendix). This is the best score ever reported for this collection. 6.3 Evaluation with the Eval05 Test Collection, Any-1 Ground Truth Lists In a fair evaluation scenario, we would use the previous experiment to train our system and choose the values of kn and kt that seem to perform the best (in particular, kn=4 and kt=0.6). Then, the system would be run and evaluated with a different collection to assess the external validity of the results and try to avoid overfitting to the training collection. For the sake of completeness, here we show the results for all 55 combinations of the parameters with the Eval05 test collection used in MIREX 2005, again aggregated with the Any-1 function [28].Table 3 shows the results. Unlike the previous experiment with the Train05 test collection, in this case the variation across levels of kt is smaller (the mean standard deviation is twice as much in Train05), indicating that the use of the time dimension does not provide better results overall (see Fig. 13). This is probably caused by the particular queries in each collection. Seven of the eleven queries in Train05 start with long rests, while this

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Table 3. Mean ADR scores for each combination of kn and kt with the Eval05 test collection, ground truth lists aggregated with the Any-1 function. kp is kept to 1. Bold face for largest scores per row and italics for largest scores per column.

kt=0 0.6522 0.653 0.6413 0.6269 0.5958

kt=0.1 0.6601 0.653 0.6367 0.6251 0.623

kt=0.2 0.6646 0.6567 0.6327 0.6225 0.6189

kt=0.3 0.6612 0.6616 0.6303 0.6168 0.6163

kt=0.4 0.664 0.6629 0.6284 0.6216 0.6162

kt=0.5 0.6539 0.6633 0.6328 0.6284 0.6192

kt=0.6 0.6566 0.6617 0.6478 0.6255 0.6215

kt=0.7 0.6576 0.6569 0.6461 0.6192 0.6174

kt=0.8 0.6591 0.65 0.6419 0.6173 0.6148

kt=0.9 0.6606 0.663 0.6414 0.6144 0.6112

kt=1 0.662 0.6531 0.6478 0.6243 0.6106

0.61

0.63

0.65

kn = 3 kn = 4 kn = 5 kn = 6 kn = 7

0.59

Mean ADR score

0.67

kn 3 4 5 6 7

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Fig. 13. Mean ADR scores for each combination of kn and kt with the Eval05 test collection, ground truth lists aggregated with the Any-1 function. kp is kept to 1

happens for only three of the eleven queries in Eval05. In our model, rests are ignored, and so the effect of the time dimension is larger when the very queries have rests as their duration is added to the next note's. Likewise, large n-grams tend to perform worse. In this case though, n-grams of length kn=3 and kn=4 perform the best. The most effective combination is kn=3 and kt=0.2, with a mean ADR score of 0.6646. However, kn=4 and kt=0.5 is very close, with a mean ADR score of 0.6633. Therefore, based on the results of the previous experiment and the results in this one, we believe that kn=4 and kt∈[0.5, 0.6] are the best parameters overall. It is also important to note that none of the 55 combinations ran result in a mean ADR score less than 0.594, which was the highest score achieved in the actual MIREX 2005 evaluation with the Any-1 ground truths [28]. Therefore, our systems would have ranked first if participated.

7 Conclusions and Future Work We have proposed a new transposition and time scale invariant model to represent musical pieces and compute their melodic similarity. Songs are considered as curves in the pitch-time plane, allowing us to compute their melodic similarity in terms of the shape similarity of the curves they define. We have implemented it with a local

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alignment algorithm over sequences of spline spans, each of which is represented by one polynomial for the pitch dimension and another polynomial for the time dimension. This parametric representation of melodies permits the application of a weight scheme between pitch and time dissimilarities. The MIREX 2005 test collections have been used to evaluate the model for several span lengths and weight schemes. Overall, spans 4 notes long seem to perform the best, with longer spans performing gradually worse. The optimal weigh scheme we found gives about twice as much importance to the pitch dimension than to the time dimension. However, time dissimilarities need to be normalized, as they are shown to be about five times smaller than pitch dissimilarities. This model obtains the best mean ADR score ever reported for the MIREX 2005 training collection, and every span length and weight scheme evaluated would have ranked first in the actual evaluation of that edition. However, the use of the time dimension did not improve the results significantly for the evaluation collection. On the other hand, three systems derived from this model were submitted to the MIREX 2010 edition: PitchDeriv, ParamDeriv and Shape [27]. These systems obtained the best results, and they ranked the top three in this edition. Again, the use of the time dimension was not shown to improve the results. A rough analysis of the MIREX 2005 and 2010 collections shows that the queries used in the 2005 training collection have significantly more rests than in the evaluation collection, and they are virtually absent in the 2010 collection. Because our model ignores rests, simply adding their durations to the next note’s duration, the use of the time dimension is shown to improve the results only in the 2005 training collection. This evidences the need for larger and more heterogeneous test collections for the Symbolic Melodic Similarity task, for researchers to train and tune their systems properly and reduce overfitting to particular collections[9][29]. The results indicate that this line of work is certainly promising. Further research should address the interpolation method to use, different ways of splitting the curve into spans, extend the model to consider rests and polyphonic material, and evaluate on more heterogeneous collections.

References 1. Aloupis, G., Fevens, T., Langerman, S., Matsui, T., Mesa, A., Nuñez, Y., Rappaport, D., Toussaint, G.: Algorithms for Computing Geometric Measures of Melodic Similarity. Computer Music Journal 30(3), 67–76 (2006) 2. Bainbridge, D., Dewsnip, M., Witten, I.H.: Searching Digital Music Libraries. Information Processing and Management 41(1), 41–56 (2005) 3. de Boor, C.: A Practical guide to Splines. Springer, Heidelberg (2001) 4. Bozkaya, T., Ozsoyoglu, M.: Indexing Large Metric Spaces for Similarity Search Queries. ACM Transactions on Database Systems 24(3), 361–404 (1999) 5. Byrd, D., Crawford, T.: Problems of Music Information Retrieval in the Real World. Information Processing and Management 38(2), 249–272 (2002) 6. Casey, M.A., Veltkamp, R.C., Goto, M., Leman, M., Rhodes, C., Slaney, M.: ContentBased Music Information Retrieval: Current Directions and Future Challenges. Proceedings of the IEEE 96(4), 668–695 (2008)

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7. Clifford, R., Christodoulakis, M., Crawford, T., Meredith, D., Wiggins, G.: A Fast, Randomised, Maximal Subset Matching Algorithm for Document-Level Music Retrieval. In: International Conference on Music Information Retrieval, pp. 150–155 (2006) 8. Doraisamy, S., Rüger, S.: Robust Polyphonic Music Retrieval with N-grams. Journal of Intelligent Systems 21(1), 53–70 (2003) 9. Downie, J.S.: The Scientific Evaluation of Music Information Retrieval Systems: Foundations and Future. Computer Music Journal 28(2), 12–23 (2004) 10. Downie, J.S., West, K., Ehmann, A.F., Vincent, E.: The 2005 Music Information Retrieval Evaluation Exchange (MIREX 2005): Preliminary Overview. In: International Conference on Music Information Retrieval, pp. 320–323 (2005) 11. Hanna, P., Ferraro, P., Robine, M.: On Optimizing the Editing Algorithms for Evaluating Similarity Between Monophonic Musical Sequences. Journal of New Music Research 36(4), 267–279 (2007) 12. Hanna, P., Robine, M., Ferraro, P., Allali, J.: Improvements of Alignment Algorithms for Polyphonic Music Retrieval. In: International Symposium on Computer Music Modeling and Retrieval, pp. 244–251 (2008) 13. Isaacson, E.U.: Music IR for Music Theory. In: The MIR/MDL Evaluation Project White paper Collection, 2nd edn., pp. 23–26 (2002) 14. Kilian, J., Hoos, H.H.: Voice Separation — A Local Optimisation Approach. In: International Symposium on Music Information Retrieval, pp. 39–46 (2002) 15. Lin, H.-J., Wu, H.-H.: Efficient Geometric Measure of Music Similarity. Information Processing Letters 109(2), 116–120 (2008) 16. McAdams, S., Bregman, A.S.: Hearing Musical Streams. In: Roads, C., Strawn, J. (eds.) Foundations of Computer Music, pp. 658–598. The MIT Press, Cambridge (1985) 17. Mongeau, M., Sankoff, D.: Comparison of Musical Sequences. Computers and the Humanities 24(3), 161–175 (1990) 18. Selfridge-Field, E.: Conceptual and Representational Issues in Melodic Comparison. Computing in Musicology 11, 3–64 (1998) 19. Smith, L.A., McNab, R.J., Witten, I.H.: Sequence-Based Melodic Comparison: A Dynamic Programming Approach. Computing in Musicology 11, 101–117 (1998) 20. Smith, T.F., Waterman, M.S.: Identification of Common Molecular Subsequences. Journal of Molecular Biology 147(1), 195–197 (1981) 21. Typke, R., den Hoed, M., de Nooijer, J., Wiering, F., Veltkamp, R.C.: A Ground Truth for Half a Million Musical Incipits. Journal of Digital Information Management 3(1), 34–39 (2005) 22. Typke, R., Veltkamp, R.C., Wiering, F.: A Measure for Evaluating Retrieval Techniques based on Partially Ordered Ground Truth Lists. In: IEEE International Conference on Multimedia and Expo., pp. 1793–1796 (2006) 23. Typke, R., Veltkamp, R.C., Wiering, F.: Searching Notated Polyphonic Music Using Transportation Distances. In: ACM International Conference on Multimedia, pp. 128–135 (2004) 24. Typke, R., Wiering, F., Veltkamp, R.C.: A Survey of Music Information Retrieval Systems. In: International Conference on Music Information Retrieval, pp. 153–160 (2005) 25. Uitdenbogerd, A., Zobel, J.: Melodic Matching Techniques for Large Music Databases. In: ACM International Conference on Multimedia, pp. 57–66 (1999) 26. Ukkonen, E., Lemström, K., Mäkinen, V.: Geometric Algorithms for Transposition Invariant Content-Based Music Retrieval. In: International Conference on Music Information Retrieval, pp. 193–199 (2003)

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27. Urbano, J., Lloréns, J., Morato, J., Sánchez-Cuadrado, S.: MIREX 2010 Symbolic Melodic Similarity: Local Alignment with Geometric Representations. Music Information Retrieval Evaluation eXchange (2010) 28. Urbano, J., Marrero, M., Martín, D., Lloréns, J.: Improving the Generation of Ground Truths based on Partially Ordered Lists. In: International Society for Music Information Retrieval Conference, pp. 285–290 (2010) 29. Urbano, J., Morato, J., Marrero, M., Martín, D.: Crowdsourcing Preference Judgments for Evaluation of Music Similarity Tasks. In: ACM SIGIR Workshop on Crowdsourcing for Search Evaluation, pp. 9–16 (2010) 30. Ó Maidín, D.: A Geometrical Algorithm for Melodic Difference. Computing in Musicology 11, 65–72 (1998)

Appendix: Results with the Original All-2 Ground Truth Lists Here we list the results of all 55 combinations of kn and kt evaluated with the very original Train05 (see Table 4) and Eval05 (see Table 5) test collections, ground truth lists aggregated with the All-2 function [21][28]. These numbers permit a direct comparison with previous studies that used these ground truth lists as well. The qualitative results remain the same: kn=4 seems to perform the best, and the effect of the time dimension is much larger in the Train05 collection. Remarkably, in Eval05 kn=4 outperforms all other n-gram lengths for all but two levels of kt. Table 4. Mean ADR scores for each combination of kn and kt with the Train05 test collection, ground truth lists aggregated with the original All-2 function. kp is kept to 1. Bold face for largest scores per row and italics for largest scores per column. kn 3 4 5 6 7

kt=0 0.7743 0.7836 0.7844 0.7885 0.7598

kt=0.1 0.7793 0.7899 0.7867 0.7842 0.7573

kt=0.2 0.788 0.7913 0.7937 0.7891 0.7466

kt=0.3 0.7899 0.7955 0.7951 0.7851 0.7409

kt=0.4 0.7893 0.7946 0.7944 0.7784 0.7186

kt=0.5 0.791 0.8012 0.7872 0.7682 0.7205

kt=0.6 0.7936 0.8039 0.7799 0.7658 0.7184

kt=0.7 0.7864 0.8007 0.7736 0.762 0.7168

kt=0.8 0.7824 0.791 0.7692 0.7572 0.711

kt=0.9 0.777 0.7919 0.7716 0.7439 0.7075

kt=1 0.7686 0.7841 0.7605 0.7388 0.6997

Table 5. Mean ADR scores for each combination of kn and kt with the Eval05 test collection, ground truth lists aggregated with the original All-2 function. kp is kept to 1. Bold face for largest scores per row and italics for largest scores per column.

kn 3 4 5 6 7

kt=0 0.7185 0.7242 0.7114 0.708 0.6548

kt=0.1 0.714 0.7268 0.7108 0.7025 0.6832

kt=0.2 0.7147 0.7291 0.6988 0.6887 0.6818

kt=0.3 0.7116 0.7316 0.6958 0.6693 0.6735

kt=0.4 0.712 0.7279 0.6942 0.6701 0.6614

kt=0.5 0.7024 0.7282 0.6986 0.6743 0.6594

kt=0.6 0.7056 0.7263 0.7109 0.6727 0.6604

kt=0.7 0.7067 0.7215 0.7054 0.6652 0.6552

kt=0.8 0.708 0.7002 0.6959 0.6612 0.6525

kt=0.9 0.7078 0.7108 0.6886 0.6561 0.6484

kt=1 0.7048 0.7032 0.6914 0.6636 0.6499

It can also be observed that the results would again be overestimated by as much as 11% in the case of Train05 and as much as 13% in Eval05, in contrast with the maximum 12% observed with the systems that participated in the actual MIREX 2005 evaluation.

Content-Based Music Discovery Dirk Schönfuß mufin GmbH August-Bebel-Straße 36,  01219 Dresden Germany [email protected]

Abstract. Music recommendation systems have become a valuable aid for managing large music collections and discovering new music. Our contentbased recommendation system employs signal-based features and semantic music attributes generated using machine-based learning algorithms. In addition to playlist generation and music recommendation, we are exploring new usability concepts made possible by the analysis results. Functionality such as the mufin vision sound universe enables the user to discover his own music collection or even unknown catalogues in a new, more intuitive way. Keywords: music, visualization, recommendation, cloud, clustering, semantic attributes, auto-tagging.

1 Introduction The way music is consumed today has been changed dramatically by its increasing availability in digital form. Online music shops have replaced traditional stores and music collections are increasingly kept on electronic storage systems and mobile devices instead of physical media on a shelf. It has become much faster and more comfortable to find and acquire music of a known artist. At the same time it has become more difficult to find one’s way in the enormous range of music that is offered commercially, find music according to one’s taste or even manage one’s own collection. Young people today have a music collection with an average size of 8,159 tracks [1] and the iTunes music store today offers more than 10 million tracks for sale. Long-tail sales are low which is illustrated by the fact that only 1% of the catalog tracks generate 80% of sales [2][3]. A similar effect can also be seen in the usage of private music collections. According to our own studies, only few people are actively working with manual playlists because they consider this too time-consuming or they have simply forgotten which music they actually possess. 1.1 Automatic Music Recommendation This is where music recommendation technology comes in: The similarity of two songs can be mathematically calculated based on their musical attributes. Thus, for each song a ranked list of similar songs from the catalogue can be generated. Editorial, user-generated or content-based data derived directly from the audio signal can be used. S. Ystad et al. (Eds.): CMMR 2010, LNCS 6684, pp. 356–360, 2011. © Springer-Verlag Berlin Heidelberg 2011

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Editorial data allows a very thorough description of the musical content but this manual process is very expensive and time-consuming and will only ever cover a small percentage of the available music. User-data based recommendations have become very popular through vendors such as Amazon (“People who bought this item also bought …”) or Last.FM. However, this approach suffers from a cold-start problem and its strong focus on popular content. Signal-based recommenders are not affected by popularity, sales rank or user activity. They extract low-level features directly from the audio signal. This also offers additional advantages such as being able to work without a server connection and being able to process any music file even if it has not been published anywhere. However, signal-based technology alone misses the socio-cultural aspect which is not present in the audio signal and it also cannot address current trends or lyrics content. 1.2 Mufin’s Hybrid Approach Mufin’s technology is signal-based but it combines signal features with semantic musical attributes and metadata from other data sources thus forming a hybrid recommendation approach. First of all, the technology analyzes the audio signal and extracts signal features. mufin then employs state-of-the-art machine learning technology to extract semantic musical attributes including mood descriptions such as happy, sad, calm or aggressive but also other descriptive tags such as synthetic, acoustic, presence of electronic beats, distorted guitars, etc. This information can for instance be used to narrow down search results, offer browsing capabilities or contextually describe content. By combining these attributes with information from other sources such as editorial metadata the user can for instance search for "aggressive rock songs from the 1970s with a track-length of more than 8 minutes". Lists of similar songs can then be generated by combining all available information including signal features, musical attributes (auto-tags) and metadata from other data sources using a music ontology. This ensures a high quality and enables the steering of the recommendation system. The results can be used for instance for playlist generation or as an aid to an editor who needs an alternative to a piece of music he is not allowed to use in a certain context. As the recommendation system features a module based on digital signalprocessing algorithms it can generate musical similarity for all songs within a music catalogue and because it makes use of mathematical analysis of the audio signals, it is completely deterministic and - if desired - can work independent of any "human factor" like cultural background, listening habits, etc. Depending on the database used, it can also work way off the mainstream and thus give the user the opportunity to discover music he may never have found otherwise. In contrast to other technologies such as collaborative filtering, mufin's technology can provide recommendations for any track, even if there are no tags or social data. Recommendations are not limited by genre boundaries, target groups or biased by popularity. Instead, it equally covers all songs in a music catalogue and if genre boundaries or the influence of popularity is indeed desired, this can addressed by leveraging additional data sources.

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Fig. 1. The mufin music recommender combines audio features inside a song model and semantic musical attributes using a music ontology. Additionally, visualization coordinates for the mufin vision sound galaxy are generated during the music analysis process.

Mufin’s complete music analysis process is fully automated. The technology has already proven its practical application in usage scenarios with more than 9 million tracks. Mufin's technology is available for different platforms including Linux, MacOS X, Windows and mobile platforms. Additionally, it can also be used via web services.

2 Mufin Vision Common, text-based attributes such as title or artist are not suitable to keep track of a large music collection, especially if the user is not familiar with every item in the collection. Songs which belong together from a sound perspective may appear very far apart when using lists sorted by metadata. Additionally, only a limited number of songs will fit onto the screen preventing the user from actually getting an overview of his collection. mufin vision has been developed with the goal to offer easy access to music collections. Even if there are thousands of songs, the user can easily find his way around the collection since he can learn where to find music with a certain characteristic. By looking at the concentration of dots in an area he can immediately assess the distribution of the collection and zoom into a section to get a closer look. The mufin vision 3D sound galaxy displays each song as a dot in a coordinate system. X, y and z axis as well as size and color of the dots can be assigned to different musical criteria such as tempo, mood, instrumentation or type of singing voice; even metadata such as release date or song duration can be used. Using the axis configuration, the user can explore his collection the way he wants and make relations between different songs visible. As a result, it becomes much easier to find music fitting a mood or occasion. Mufin vision premiered in the mufin player PC application but it can also be used on the web and even on mobile devices. The latest version of the mufin player 1.5 allows the user to control mufin vision using a multi-touch display.

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Fig. 2. Both songs are by the same artist. However, “Brothers in arms” is a very calm ballad with sparse instrumentation while “Sultans of swing” is a rather powerful song with a fuller sound spectrum. The mufin vision sound galaxy reflects that difference since it works on song level instead of an artist or genre level.

Fig. 3. The figure displays a playlist in which the entries are connected by lines. One can see that although the songs may be similar as a whole, their musical attributes vary over the course of the playlist.

3 Further Work The mufin player PC application offers a database view of the user’s music collection including filtering, searching and sorting mechanisms. However, instead of only using metadata such as artist or title for sorting, the mufin player can also sort any list by similarity to a selected seed song.

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Additionally, the mufin player offers an online storage space for a user’s music collection. This prevents the user from data loss and allows him to simultaneously stream his music online and listen to it from anywhere in the world. Furthermore, mufin works together with the German National Library in order to establish a workflow for the protection of our cultural heritage. The main contribution of mufin is the fully automatic annotation of the music content and the provision of descriptive tags for the library’s ontology. Based on technology by mufin and its partners, a semantic multimedia search demonstration was presented at IBC 2009 in Amsterdam.

References 1. 2. 3.

Bahanovich, D., Collopy, D.: Music Experience and Behaviour in Young People. University of Hertfordshire, UK (2009) Celma, O.: Music Recommendation and Discovery in the Long Tail. PhD-Thesis, Universitat Pompeu Fabra, Spain (2008) Nielsen Soundscan: State of the industrie (2007), http://www.narm.com/ 2008Conv/StateoftheIndustry.pdf (July 22, 2009)

Author Index

Abeßer, Jakob 259 Alvaro, Jes´ us L. 163 Anglade, Am´elie 1 Aramaki, Mitsuko 176 Arcos, Josep Lluis 219

Mansencal, Boris 31 Marchand, Sylvain 31 Marchini, Marco 205 Mauch, Matthias 1 Merer, Adrien 176 Morato, Jorge 338 Mustafa, Haﬁz 84

Barbancho, Ana M. 116 Barbancho, Isabel 116 Barros, Beatriz 163 Barthet, Mathieu 138 Bunch, Pete 76 Cano, Estefan´ıa

N¨ urnberger, Andreas

Ortiz, Andr´es 116 ¨ Ozaslan, Tan Hakan 219 Ozerov, Alexey 102

259

de Haas, W. Bas 242 de la Bandera, Cristina 116 Dittmar, Christian 259 Dixon, Simon 1

Palacios, Eric 219 Purwins, Hendrik 205 Rigaux, Philippe 303 R¨ obel, Axel 60 Robine, Matthias 242 Rodet, Xavier 60 Romito, Marco 60

Faget, Zoe 303 F´evotte, C´edric 102 Girin, Laurent 31 Godsill, Simon 76 Grollmisch, Sascha 259 Großmann, Holger 259 Guaus, Enric 219

Sammartino, Simone 116 S´ anchez-Cuadrado, Sonia 338 Sandler, Mark 20, 138 Sch¨ onfuß, Dirk 356 Stewart, Rebecca 20 Stober, Sebastian 273

Hanna, Pierre 242 Hargreaves, Steven 138 Jensen, Kristoﬀer

51

Karvonen, Mikko 321 Klapuri, Anssi 188 Kronland-Martinet, Richard Kudumakis, Panos 20 Laitinen, Mika 321 Lemstr¨ om, Kjell 321 Liuni, Marco 60 Llor´ens, Juan 338 Lukashevich, Hanna 259

273

Tard´ on, Lorenzo J. Urbano, Juli´ an 176

338

Veltkamp, Remco C. Vikman, Juho 321 Wang, Wenwu Wiering, Frans Ystad, Sølvi

116

84 242 176

242