On efficient music genre classification

Authors:
Jialie Shen;John Shepherd;Anne H. H. Ngu
Affiliations:
School of Computer Sci. and Eng., University of New South Wales, Sydney, NSW, Australia;School of Computer Sci. and Eng., University of New South Wales, Sydney, NSW, Australia;Department of Computer Sci., Texas State University, San Marcos, Texas
Venue:
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
Year:
2005

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Abstract

Automatic music genre classification has long been an important problem. However, there is a paucity of literature that addresses the issue, and in addition, reported accuracy is fairly low. In this paper, we present empirical study of a novel music descriptor generation method for efficient content based music genre classification. Analysis and empirical evidence demonstrate that our approach outperforms state-of-the-art approaches in the areas including accuracy of genre classification with various machine learning algorithms, efficiency on training process. Furthermore, its effectiveness is robust against various kinds of audio alternation.