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Audio genre classification using percussive pattern clustering combined with timbral features

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4 Author(s)
Emiru Tsunoo ; Graduate School of Information Science and Technology, The University of Tokyo, Japan ; George Tzanetakis ; Nobutaka Ono ; Shigeki Sagayama

Many musical genres and styles are characterized by distinct representative rhythmic patterns. In most automatic genre classification systems global statistical features based on timbral dynamics such as mel-frequency cepstral coefficients (MFCC) are utilized but so far rhythmic information has not so effectively been used. In order to extract bar-long unit rhythmic patterns for a music collection we propose a clustering method based on one-pass dynamic programming and k-means clustering. After extracting the fundamental rhythmic patterns for each style/genre a pattern occurrence histogram is calculated and used as a feature vector for supervised learning. Experimental results show that the automatically calculated rhythmic pattern information can be used to effectively classify musical genre/style and improve upon current approaches based on timbral features.

Published in:

2009 IEEE International Conference on Multimedia and Expo

Date of Conference:

June 28 2009-July 3 2009