Metadata for Audio 25th International AES Conference 17th to 19th June 2004 London UK
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Paper CD6-2

Assessing the Relevance of Rhythmic Descriptors in a Musical Genre Classification Task

Fabien Gouyon1, Simon Dixon2, Elias Pampalk2, Gerhard Widmer2
1Universitat Pompeu Fabra, Barcelona, Spain
2Austrian Research Institute for AI, Vienna, Austria

Organizing or browsing music collections in a musically meaningful way calls for tagging the data in terms of, e.g., rhythmic, melodic or harmonic aspects, among others. In some cases, such metadata can be extracted automatically from musical files; in others, a trained listener must extract it by hand. In this paper we consider a specific set of rhythmic descriptors for which we provide procedures of automatic extraction from audio signals. Evaluating the relevance of such descriptors is a difficult task that can easily become highly subjective. To avoid this pitfall, we assessed the relevance of these descriptors by measuring their rate of success in genre classification experiments.

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