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http://dx.doi.org/10.7776/ASK.2014.33.1.081

A Musical Genre Classification Method Based on the Octave-Band Order Statistics  

Seo, Jin Soo (Department of Electronic Engineering, Gangneung-Wonju National University)
Publication Information
The Journal of the Acoustical Society of Korea / v.33, no.1, 2014 , pp. 81-86 More about this Journal
Abstract
This paper presents a study on the effectiveness of using the spectral and the temporal octave-band order statistics for musical genre classification. In order to represent the relative disposition of the harmonic and non-harmonic components, we utilize the octave-band order statistics of power spectral distribution. Experiments on the widely used two music datasets were performed; the results show that the octave-band order statistics improve genre classification accuracy by 2.61 % for one dataset and 8.9 % for another dataset compared with the mel-frequency cepstral coefficients and the octave-band spectral contrast. Experimental results show that the octave-band order statistics are promising for musical genre classification.
Keywords
Music classification; Genre classification; Order statistics;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 G. Tzanetakis and P. Cook, "Musical genre classification of audio signals," IEEE Trans. Speech and Audio Process. 10, 293-302 (2002).   DOI   ScienceOn
2 Y. Panagakis, C. Kotropoulos, and G. Arce, "Non-negative multilinear principal component analysis of auditory temporal modulations for music genre classification," IEEE Trans. Audio Speech Lang. Process. 18, 576-588 (2010).   DOI
3 D. Jiang, L. Lu, H. Zhang, J. Tao, and L. Cai, "Music type classification by spectral contrast feature," in Proc. ICME-2002, 113-116 (2002).
4 S.-C. Lim, S.-J. Jang, S.-P. Lee, and M. Y. Kim, "Music genre classification system using decorrelated filter bank," (in Korean) J. Acoust. Soc. Kr. 30, 100-106 (2011).   과학기술학회마을   DOI
5 A. Meng, P. Ahrendt, J. Larsen, and L. Hansen, "Temporal feature integration for music genre classification," IEEE Trans. Audio Speech Lang. Process. 15, 1654 - 1664 (2007).   DOI   ScienceOn
6 E. Pampalk, A. Flexer, and G. Widmer, "Improvements of audio-based music similarity and genre classification," in Proc. ISMIR-2005, 634-637 (2005).
7 P. Loizou and O. Poroy, "Minimum spectral contrast needed for vowel identification by normal-hearing and cochlear implant listeners," J. Acoust. Soc. Am. 110, 1619-1627 (2001).   DOI   ScienceOn
8 J. Seo and S. Lee, "Higher-order moments for musical genre classification," Signal Processing 91, 2154-2157 (2011).   DOI
9 S.-C. Lim, J.-S. Lee, S.-J. Jang, S.-P. Lee, and M. Kim, "Music-genre classification system based on spectro-temporal features and feature selection," IEEE Trans. Consum. Electron. 58, 1262-1268 (2012).   DOI   ScienceOn