1. A shallow description framework for musical style recognition Pedro J. Ponce de León, Carlos Pérez-Sancho and José Manuel Iñesta Departamento de Lenguajes y Sistemas Informáticos Universidad de Alicante {pierre,cperez,inesta}@dlsi.ua.es One challenging task for pattern recognition algorithms in the field of computer music is the automatic recognition of musical style, that has a number of applications like indexing and selecting musical databases in music information retrieval (MIR). In this paper, the classification of monophonic melodies of two different musical styles (jazz and classical) represented symbolically as MIDI files is studied, using different classification methods: Bayesian classier and nearest neighbour classier. From the music sequences, a number of melodic, harmonic, and rhythmic statistical descriptors are computed and used for style recognition. We present a performance analysis of such algorithms against different description models and parameters. Abstract This work has been funded by the Spanish CICyT project TIRIG; code TIC2003-08496-C04-04 Pattern recognition algorithms are applicable to a number of computer music research tasks. E.g.: content-based organisation, indexing, and exploration of digital music databases (digital music libraries). Sound data (WAV, MP3, etc.) and Symbolic data (digital scores, MIDI, MusicXML, etc.) Modelization of the music style. The computer could be trained in the user musical taste in order to look for that kind of music over large musical databases. Pattern recognition algorithms are applicable to a number of computer music research tasks. E.g.: content-based organisation, indexing, and exploration of digital music databases (digital music libraries). Sound data (WAV, MP3, etc.) and Symbolic data (digital scores, MIDI, MusicXML, etc.) Modelization of the music style. The computer could be trained in the user musical taste in order to look for that kind of music over large musical databases. Introduction To develop a system able to distinguish musical styles from a symbolic representation of melodies: 1)Proposal of melodic, harmonic, and rhythmic statistical descriptors. 2)Jazz vs. Classical music classification. 3)To explore the method performance for different classification algorithms, descriptor models, and parameter values. To develop a system able to distinguish musical styles from a symbolic representation of melodies: 1)Proposal of melodic, harmonic, and rhythmic statistical descriptors. 2)Jazz vs. Classical music classification. 3)To explore the method performance for different classification algorithms, descriptor models, and parameter values. Objectives: Training set: Files were collected from the Internet without pre-processing before input to the system. From each file, the melody (monophonic) is extracted and descriptors are extracted. Training set: Files were collected from the Internet without pre-processing before input to the system. From each file, the melody (monophonic) is extracted and descriptors are extracted. Music data We have shown the ability of two classifiers to map symbolic representations of melodic segments into a set of musical styles using melodic, harmonic and rhythmic statistical descriptors. Both Bayes and NN classifiers perform comparatively well and reach a success of 94% for the problem of a classification of jazz versus classical music. The average was of a 83.3%. The best performances were found for large window sizes and small or moderate displacements. Larger corpora and different styles are currently under study. Other classification schemes are going to be tested. Also voting schemes and classifier ensembles will be tested. We have shown the ability of two classifiers to map symbolic representations of melodic segments into a set of musical styles using melodic, harmonic and rhythmic statistical descriptors. Both Bayes and NN classifiers perform comparatively well and reach a success of 94% for the problem of a classification of jazz versus classical music. The average was of a 83.3%. The best performances were found for large window sizes and small or moderate displacements. Larger corpora and different styles are currently under study. Other classification schemes are going to be tested. Also voting schemes and classifier ensembles will be tested. 4. Conclusions Approach considered in this work MIDI filesLength in measuresAuthors 455227 Mozart, Bach, Schubert, Chopin, Grieg, Vivaldi, Schumann, Brahms, Beethoven, Dvorak, Haendel, Paganini and Mendelssohn. 654734 Charlie Parker, Duke Ellington, Bill Evans, Miles Davis, etc. Description by sliding window: The windows have a length of  measures and are displaced  measures each time. The melodic fragments are described in terms of 22 descriptors of three different kinds: Feature selection and descriptor models: After a feature selection procedure, those descriptors having a greater discrimination power have been grouped into models: Description by sliding window: The windows have a length of  measures and are displaced  measures each time. The melodic fragments are described in terms of 22 descriptors of three different kinds: Feature selection and descriptor models: After a feature selection procedure, those descriptors having a greater discrimination power have been grouped into models: Descriptors ModelDescriptors 6 Pitch range, max. interval, dev. note duration, max. note duration, dev. pitch, avg. note duration 7 + syncopation 10 + avg. pitch, dev. of intervals, number of notes 13 + number of silences, min. interval, num. non-diatonic notes 22 All the descriptors 51.0 52.0 65.0 77.0 70.8 3.0 2.0 10.0 4.8 2.3 1.0 21.0 2.7 3.8 0.0 9.0 2.9 2.0 6.0 2.2 1.6 5.8 49.0 50.0 65.0 77.0 70.8 2.9 2.0 10.0 4.8 2.3 1.0 25.0 3.0 4.8 0.0 9.0 2.8 2.0 6.0 2.2 1.6 3.3 47.0 60.0 77.0 67.8 3.8 2.0 19.0 5.4 3.5 1.0 21.0 2.8 3.9 1.0 9.0 3.3 1.8 9.0 1.7 1.2 4.5 Descriptors Data Classification Overall features (2): number of notes and silences. Pitch features (4): Note duration (4): min., max., mean and standard deviations Silence duration (4): Intervals (4): Non diatonic notes (3): number, average and standard deviation Melodic descriptors: Harmonic descriptors: Syncopation (1): estimated number of syncopations. Rhythmic descriptor: Width (  ): number of measures described Displacement (  ): number of measures the window is shifted forward Given a melody composed of m measures, the number of segments obtained from it is For example: The number of segments extracted from the training set for the point  =3;  =1 is 8985. For  =100;  =20 is only 119. Width (  ): number of measures described Displacement (  ): number of measures the window is shifted forward Given a melody composed of m measures, the number of segments obtained from it is For example: The number of segments extracted from the training set for the point  =3;  =1 is 8985. For  =100;  =20 is only 119. Window parameters Bayesian + Nearest neighbours 10-fold crossvalidation: results presented are average of each 10 sub-experiments 2 classifiers, 5 models Window parameter space:   1,...,100,    1,...,  2275 points A total of 22750 experiments were performed Bayesian + Nearest neighbours 10-fold crossvalidation: results presented are average of each 10 sub-experiments 2 classifiers, 5 models Window parameter space:   1,...,100,    1,...,  2275 points A total of 22750 experiments were performed Classifiers Parameter space Classification performances k-NNBayes Parameter space Classification performances k-NNBayes Classification results   model classifier Worst zone: Small window and displacements Best zone: large window and small displacements < 80% > 92%