A class centric feature and classifier ensemble selection approach for music genre classification

Authors:
Hasitha Bimsara Ariyaratne;Dengsheng Zhang;Guojun Lu
Affiliations:
Gippsland School of IT, Monash University, Churchill, Australia;Gippsland School of IT, Monash University, Churchill, Australia;Gippsland School of IT, Monash University, Churchill, Australia
Venue:
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Year:
2012

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Abstract

Music genre classification has attracted a lot of research interest due to the rapid growth of digital music. Despite the availability of a vast number of audio features and classification techniques, genre classification still remains a challenging task. In this work we propose a class centric feature and classifier ensemble selection method which deviates from the conventional practice of employing a single, or an ensemble of classifiers trained with a selected set of audio features. We adopt a binary decomposition technique to divide the multiclass problem into a set of binary problems which are then treated in a class specific manner. This differs from the traditional techniques which operate on the naive assumption that a specific set of features and/or classifiers can perform equally well in identifying all the classes. Experimental results obtained on a popular genre dataset and a newly created dataset suggest significant improvements over traditional techniques.