Fractal dimension and classification of music
Identifieur interne : 000A32 ( Main/Exploration ); précédent : 000A31; suivant : 000A33Fractal dimension and classification of music
Auteurs : M. Bigerelle [France] ; A. Iost [France]Source :
- Chaos, Solitons and Fractals: the interdisciplinary journal of Nonlinear Science, and Nonequilibrium and Complex Phenomena [ 0960-0779 ] ; 2000.
English descriptors
- Teeft :
- Acoustic, Anam, Anam method, Audio signal, Bigerelle, Dimension, Duncan grouping, Electronic music, Error correction, Fractal, Fractal aspect, Fractal dimension, Fractal dimensions, Iost, Iost chaos, Lower frequency, Nonlinear, Nonlinear regression, Prob, Progressive music, Relaxation music, Sample rate, Soliton, String quartet, Traditional music, Variance, Variation method, Voss, Windows size.
Abstract
Abstract: The fractal aspect of different kinds of music was analyzed in keeping with the time domain. The fractal dimension of a great number of different musics (180 scores) is calculated by the Variation method. By using an analysis of variance, it is shown that fractal dimension helps discriminate different categories of music. Then, we used an original statistical technique based on the Bootstrap assumption to find a time window in which fractal dimension reaches a high power of music discrimination. The best discrimination is obtained between 1/44100 and 16/44100 Hertz. We admit that to distinguish some different aspects of music well, the high information quantity is obtained in the high frequency domain. By calculating fractal dimension with the ANAM method, it was statistically proven that fractal dimension could distinguish different kinds of music very well: musics could be classified by their fractal dimensions.
Url:
DOI: 10.1016/S0960-0779(99)00137-X
Affiliations:
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Le document en format XML
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<front><div type="abstract" xml:lang="en">Abstract: The fractal aspect of different kinds of music was analyzed in keeping with the time domain. The fractal dimension of a great number of different musics (180 scores) is calculated by the Variation method. By using an analysis of variance, it is shown that fractal dimension helps discriminate different categories of music. Then, we used an original statistical technique based on the Bootstrap assumption to find a time window in which fractal dimension reaches a high power of music discrimination. The best discrimination is obtained between 1/44100 and 16/44100 Hertz. We admit that to distinguish some different aspects of music well, the high information quantity is obtained in the high frequency domain. By calculating fractal dimension with the ANAM method, it was statistically proven that fractal dimension could distinguish different kinds of music very well: musics could be classified by their fractal dimensions.</div>
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