Music Classification is a particular area of Computational Musicology that provides valuable insights about the evolving of compo- sition patterns and assists in catalogue generation. The proposed work detaches from former works by classifying music based on music score in- formation. Text Mining techniques support music score processing while Classification techniques are used in the construction of decision mod- els. Although research is still at its earliest beginnings, the work already provides valuable contributes to symbolic music representation process- ing and subsequent analysis. Score processing involved the counting of ascending and descending chromatic intervals, note duration and meta- information tagging. Analysis involved feature selection and the evalu- ation of several data mining algorithms, ensuring extensibility towards larger repositories or more complex problems. Experiments report the analysis of composition epochs on a subset of the Mutopia project open archive of classical LilyPond-annotated music scores

Almeida, J. J.

Lourenço, Anália

Simões, Alberto

Universidade do Minho: RepositoriUM

Using Text Mining Techniques forClassical Music Scores AnalysisAlberto Simo˜es, Ana´lia Lourenc¸o, and Jose´ Joa˜o AlmeidaDepartamento de Informa´tica, Universidade do Minho,Campus de Gualtar, 4710-057 Braga, PORTUGAL{ambs,analia,jj}@di.uminho.ptAbstract. Music Classification is a particular area of ComputationalMusicology that provides valuable insights about the evolving of compo-sition patterns and assists in catalogue generation. The proposed workdetaches from former works by classifying music based on music score in-formation. Text Mining techniques support music score processing whileClassification techniques are used in the construction of decision mod-els. Although research is still at its earliest beginnings, the work alreadyprovides valuable contributes to symbolic music representation process-ing and subsequent analysis. Score processing involved the counting ofascending and descending chromatic intervals, note duration and meta-information tagging. Analysis involved feature selection and the evalu-ation of several data mining algorithms, ensuring extensibility towardslarger repositories or more complex problems. Experiments report theanalysis of composition epochs on a subset of the Mutopia project openarchive of classical LilyPond-annotated music scores.1 IntroductionThe improving ability of score typesetting has made possible many new ap-plications in Music Information Retrieval and Statistical Musicology. Musicalcatalogues, digital audio editing, real-time performance enhancement and ac-companiment systems are now a reality [1].This work focused on the analysis of music composition, studying classicalcomposition patterns throughout different epochs. Descriptive statistics such asthe counting of notes and intervals provide an overall description of each scorewhereas statistical discriminant, factor, and cluster analyses are often appliedto the study of composition evolving and genre. Nevertheless, the analysis ofsymbolic music representation is, as far as we know, rare and may be considereda breakthrough.Open source typesets like Lilypond [5,6], abc [7] or MusicXML [3] and com-mercial typesets like Finale or Sibelius provide machine-readable representationsof music scores. Text Mining techniques can easily address the counting of as-cending and descending chromatic intervals and each note duration and otherkinds of metrics, and the tagging of meta-information. On the other hand, Fea-ture Selection and Data Mining techniques can conveniently address furtheranalysis, account for problem complexity and specific goals.Research is still at its earliest beginnings and experiments do not yet exposethe true potential of analysis. Nevertheless, text processing covers all major as-pects of symbolic representations, producing datasets suitable for further mining.Future work devises the improvement (in terms of robustness) of our Lilypondparser, the processing of polyphonic scores and the replacement of semi-tonesby musical interval notation. Besides classical music composition, analysis overother music genres (for instance, folk and jazz music) is to be addressed.2 Symbolic Music RepresentationComputational Musicology and Natural Language Processing are very similarareas. Music is a language (usually denoted as the universal language) and needsto be represented in a computer-readable format in order to be processed.This section discusses the advantages of using symbolic music representationin music analysis, and evaluates the most common formats.2.1 Audio versus Symbolic RepresentationsThere is a parallelism between the concerns of musicologists and linguists. Whenanalysing a music record from a Bach fugue, we are listening to the organist’sinterpretation of Bach’s own composition. Likewise, when listening to a recordedoral story, one perceives the emotions transmitted by the reader. Even consid-ering that interpretation does not alter the original composition, speech/audiorecognition would be an issue. On the other hand, music scores/texts are almostnoise free and are better at capturing work’s details. Linguists take advantage ofsentences boundaries and punctuation marks when analysing texts, and musicol-ogists can use scores meta-information like pitch and time signatures to enrichtheir analysis.Computational music analysis is usually based on audio (mp3, wave, aiff,midi), because audio repositories are largely available and symbolic resourcesare more scarce due to the transcription work involved. Both representationsare valid and valuable, but while audio encompasses the performer’s emotionsand particular interpretation, music scores focus on composition. In this regard,musicologists prefer to work with the latest.2.2 Music Score TypesettingThe easier way to represent a music score is using an image file. Although thisformat guarantees portability and human readability, it is impossible to processautomatically. Optical Music Recognition and Computational Musicology maybe complementary but they are independent research areas.When looking for music typesetting software one can easily find commercialsoftware like Finale or Sibelius. The problem is that their internal format formusic representation is closed and not documented making their use difficult.On the other hand, there are some open formats such as:– MusicXML [3]The most recent is MusicXML, an XML based format. It is being more andmore used, although there are still few music scores encoded with it.– abc [7] abc++ [2]These are simple textual formats that generate quality music scores and alsoMIDI files. There are some repositories with abc encoded music, but theformat is not flexible enough for some specific music notation.– Lilypond [5,6]This format was inspired in abc and MusicTEX, and is able to produce highquality music scores and MIDI files. This GNU project keeps a steady devel-opment, supporting a wide range of music notation, from ancient notationto modern music. Also, there are tools that support the conversions fromabc and MusicXML into the Lilypond notation. Yet, Lilypond has a majordrawback: a very complex and instable syntax.After evaluating overall advantages and disadvantages, Lilypond typesettingwas chosen to support the present research. Although syntax is an issue, therichness of this typesetting notation was considered an important breakthrough.Fig. 1. Music score excerpt.\relative c, {\time 3/4\clef bassc2 e8 c’ g’2.f4 e d c4 c, r4}Fig. 2. LilyPond notation example.Elaborated, real-world scores lead to complex lilypond code. It is not withinthe scope of this work to expose Lilypond’s notation at its full extent. Figure 2shows the code for a small score excerpt (Figure 1) in order to support furtherprocessing and analysis discussion.3 Music Score ProcessingMusic score processing may involve different kinds of features depending on theaim of the computational analysis. There are atomic features such as chromaticand diatonic intervals, rhythm and key signature, and compound features liken-grams of intervals and rhythmic patterns. It is also possible to extract meta-information such as instruments, performance and composition style.3.1 Chromatic IntervalsLilypond supports two notation styles: absolute and relative positioning. Usingabsolute positioning force the user to use octavation quotes to specify in whichoctave the note will be placed. In relative mode, a note without octavation quotesis chosen so that it is closest to the previous one. Since most music has smallintervals, pieces can be written almost without octavation quotes in relativemode. Larger intervals are made by adding octavation quotes.To compute chromatic intervals the notation style needs to be detected (rel-ative positioning needs a relative command) so the list of notes is normalizedusing the relative notation. Then, intervals are calculated two by two: first notewith the second, second with the third, and so on. Rests are treated specially, soan interval between the note before the rest and the one after is not calculated.Also, intervals can be ascending or descending. Thus, a positive or negative in-teger value of semi-tones is calculated for each interval using a built-in table ofconsequent notes. Table 1 shows the intervals extracted from Figure 1.Table 1. Interval calculus.interval c e e c’ c g’ g f f e e d d c c c,semi-tones 4 8 7 -2 -1 -2 -2 -12To simplify further discussion on chromatic intervals we will represent themas “an” for ascending intervals, and “amn” for descending intervals. The valuen is the number of ascending or descending semi-tones. Table 2 summarizes thenumber of occurrences for each interval.Table 2. Intervals histogram.semi-tones am12 am2 am1 a4 a8 a7occurrences 1 3 1 1 1 13.2 Diatonic IntervalsDiatonic intervals are less informative than chromatic intervals, as different chro-matic intervals correspond to the same diatonic interval. Diatonic intervals com-putation is quite similar to chromatic intervals calculation with a different tableof basic intervals.3.3 RhythmFor rhythm, Lilypond uses a numeric parameter specifying each note duration.If omitted, the note duration is the same as the previous one. A value of 1represents the Semi-breve (whole note), 2 represents the Minim (half note), 4 isused for the Semiminim (crotchet), and so on. For dotted notes, one (or more)dots can be added.Table 3. Rhythm histogram.rhythmic figure countarhythm2dot 1arhythm2 1arhythm4 6arhythm8 2To compute rhythm information the Lilypond music needs to be normalizedso each note contains its duration. Then, they can be easily extracted usingregular expressions. To help discussion, rhythmic attributes will be named as“arhythmduration”. Table 3 summarizes the occurrence count for each note du-ration from the example above.3.4 Rhythmic GesturesSome rhythmic gestures are usual in specific epochs. For instance, the use oftuplets or syncopation is relevant for epoch classification. Tuplets detection issimple as Lilypond notation includes specific commands to typeset them. Thus,a quick search for the command and analysis of its parameters suffices for tupletsfeature extraction. Syncopation and similar rhythmic gestures need to be anal-ysed taking into account figures relative duration, and their relative positioningon bars.3.5 Key SignatureAs it is known, a specific key signature does not force the tonality for a music.For instance, a key signature without any accidental can be C major or A minor.Meanwhile, Lilypond and some other symbolic notations, include some more in-formation than just the key signature. Lilypond can include the full tonality(pitch and type: major, minor, mixolydian, lydian, and others) in the key sig-nature. Also, there is the problem of key signature change. There are differentapproaches to solve this problem: counting the number of bars for each tonalityfound, or just consider the first one.3.6 Time SignatureTime signature feature extraction is similar to the key signature extraction: thereis a specific command to explicitly specify the one being used, but this commandcan be used more than once.4 Experiments on Classical Music Epoch ClassificationClassical music epoch classification was chosen as case study for this paper.While working with Lilypond typesetting and studying classical music, Mu-topia’s project 1, was the primary data source. This is a fairly large and diverseopen-source repository. There are over than 800 scores richly annotated withmeta-information.Data analysis involved dataset preparation, feature selection, algorithm eval-uation and results assessment. Dataset preparation addressed the collection ofmusic scores and their tagging according to composition time periods. Althoughmusic composition period boundaries are not consensual, it is fairly acceptableto define rough time limits and there were defined four time intervals (Table 4).Given that music scores for different instruments are not directly comparable,so far only monophonic scores for violin, cello, flute, recorder and clarinet werecollected.Table 4. Classification Epochs.Name Year Range Size ComposersA 1500–1600 5 J. des Pre´s, Banister, anonymousB 1700–1800 11 Bach, BaltzarC 1800–1900 4 Minkus, GiulianiD 1900–2000 4 Grieg, BrownMining experiments used Weka open-source data mining toolkit 2 which al-lowed not only the actual mining but also feature analysis and algorithm evalu-ation. These experiments did not aim at the full construction of a classificationmodel but instead to analyse its feasibility.Dataset attributes were evaluated in order to assess their predictive abilityusing three different statistical approaches: Support Vector Machines (SVMs),gain ratio and Chi-squared test (Table 5). As a result, two different feature sets(given the similarity between gain ratio and Chi-squared outputs) were used indata mining experiments.Data mining aimed at testing different analysis approaches, while determin-ing the set of predictive attributes more suitable for this particular classifica-tion problem. ZeroR majority voting provided a reference model, while J48,1 http://www.mutopiaproject.org/2 http://www.cs.waikato.ac.nz/~ml/weka/Table 5. Feature selection outputs.Most worthy attributesSVMAttributeEval am6, arhythm1, am24, a3, arhythm2dot, am9, am7,am2, a12, arhythm4dot, a13, a7GainRatioAttributeEval am7, am6, a12, a7, am2, a1, am12, am9, a9ChiSquaredAttributeEval am7, a12, am6, a7, am2, a1, am9, am12, am4Na¨ıve Bayes and SimpleCart applied information theory, Bayes theorem andcost-complexity analysis to the problem. The 10-fold stratified cross-validationtechnique, which is the evaluation method of choice in most practical limited-data situations [4], supported the prediction of the error rate of the learningalgorithms.Table 6. Algorithm performance over SVM proposed features.ZeroR J48 NaiveBayes SimpleCartCorrectly Classified Instances (%) 45.8333 62.5 62.5 58.3333Kappa statistic 0 0.449 0.4114 0.3352Mean absolute error (%) 0.3564 0.2024 0.2027 0.2427Root mean squared error (%) 0.4243 0.4192 0.4263 0.3899Relative absolute error (%) 100 56.7764 56.8712 68.0993Root relative squared error (%) 100 98.8108 100 91.9018ZeroR J48 NaiveBayes SimpleCartClass TP FP F1 TP FP F1 TP FP F1 TP FP F1A 0 0 0 0.4 0.105 0.444 0.2 0 0.333 0.6 0.211 0.5B 1 1 0.629 0.727 0.308 0.696 0.909 0.462 0.741 0.909 0.462 0.741C 0 0 0 1 0.15 0.727 0.5 0 0.667 0 0 0D 0 0 0 0.25 0 0.4 0.5 0.15 0.444 0.25 0 0.4Figure 3 shows a simplified tree for music classification, result of the J48model. Also it is interesting to refer that these measures are not just the obtainedusing the model, but they make sense regarding musicology analysis.5 Final RemarksThe main contributes of this work can be divided into score processing and musiccomposition analysis. Symbolic music representation was considered a far richerdata source than commonly used audio files. In this regard, Lilypond typeset-ting open-source software provided a convenient representation while Mutopiaproject delivered a considerable number of already annotated scores. Score au-tomatic processing involved score parsing towards the identification of rhythmTable 7. Algorithm performance over gain ratio and Chi-squared proposed features.ZeroR J48 NaiveBayes SimpleCartCorrectly Classified Instances (%) 45.8333 66.6667 54.1667 62.5Kappa statistic 0 0.5013 0.3449 0.4255Mean absolute error (%) 0.3564 0.1836 0.2292 0.2151Root mean squared error (%) 0.4243 0.4036 0.4461 0.3925Relative absolute error (%) 100 51.5156 64.3156 60.3612Root relative squared error (%) 100 95.1406 100 92.5063ZeroR J48 NaiveBayes SimpleCartClass TP FP F1 TP FP F1 TP FP F1 TP FP F1A 0 0 0 0.4 0.105 0.444 0.4 0.263 0.286 0.6 0.158 0.6B 1 1 0.629 0.818 0.308 0.75 0.727 0.154 0.8 0.909 0.308 0.909C 0 0 0 0.75 0.1 0.667 0.5 0 1 0.25 0.05 0.25D 0 0 0 0.5 0 0.667 0.25 0.2 0.2 0.25 0.05 0.25a12 ascending octavation;am6 descending augmented fourth;am2 descending major second;Fig. 3. Tree representation of the classical music classification model.figures and the calculation of chromatic intervals and key and time signatures.Music composition analysis included general statistical analysis and data mining,aiming at the construction of classification models. Specifically, models capableof identifying the classical music composition epoch.Although the Lilypond parser needs to be improved (in terms of robustness),future work is driven by new analysis horizons. Processing is to be extended topolyphonic scores and musical interval notation should replace semi-tones. Be-sides classical music composition, analysis over other music genres (for instance,folk and jazz music) is to be addressed.AcknowledgmentsAlberto Simo˜es has a scholarship from Fundac¸a˜o para a Computac¸a˜o Cient´ıficaNacional and the work reported here has been partially funded by Fundac¸a˜opara a Cieˆncia e Tecnologia through project POSI/PLP/43931/2001, co-financedby POSI, and by POSC project POSC/339/1.3/C/NAC. The work of Ana´liaLourenc¸o is funded by a scholarship from the Fundac¸a˜o para a Cieˆncia e Tec-nologia (Ref. POCI/BIO/60139/2004).References1. Roger B. Dannenberg and Christopher Raphael. Music score alignment and com-puter accompaniment. Commun. ACM, 49(8):38–43, 2006.2. Guido Gonzato. Making Music with ABC PLUS (v 1.0.4), 2005.http://abcplus.sourceforge.net/abcplus en-1.0.4.zip.3. Michael Good. MusicXML: an internet-friendly format for sheet music. In XMLConference & Exposition, Orlando, Florida, 2001.4. Ron Kohavi. A study of cross-validation and bootstrap for accuracy estimationand model selection. In International Joint Conference on Artificial Intelligence(IJCAI), pages 1137–1145, 1995.5. Han-Wen Nienhuys. LilyPond, automated music formatting and the art of shipping.In Forum Internacional Software Livre (FISL7.0), 2006.6. Han-Wen Nienhuys and Jan Nieuwenhuizen. LilyPond, a system for automatedmusic engraving. In Proceedings of the XIV Colloquium on Musical Informatics,Firenze, Italy, May 2003.7. I. Oppenheim. The ABC Music standard 2.0 (draft IV, 14/8/2003), 2003.http://abc.sourceforge.net/standard/abc2-draft.html.

Using text mining techniques for classical music scores analysis

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Using text mining techniques for classical music scores analysis

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