A class centric feature and classifier ensemble selection approach for music genre classification
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Classification accuracy is not enough
Journal of Intelligent Information Systems
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Automatic music genre classification is an important component in Music Information Retrieval (MIR). It has gained lot of attention lately due to the rapid growth in the use of digital music. Past work in this area has already produced a number of audio features and classification techniques, however, genre classification still remains an unsolved problem. In this paper we explore a hybrid unsupervised/supervised top-down hierarchical classification approach. Most existing work on hierarchical music genre classification relies on human built trees and taxonomies, however these hierarchies may not always translate well into machine classification problems. Therefore, we explore an automatic approach to construct a classification tree through subspace cluster analysis. Experimental results validate the tree building algorithm and provide a new research direction for automatic genre classification. We also addressed the issue of scarcity in publicly available music datasets, by introducing a new dataset containing genre, artist and album labels.