Metadata for Audio
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Paper CD6-2
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Assessing the Relevance of Rhythmic Descriptors
in a Musical Genre Classification Task
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Fabien
Gouyon1, Simon Dixon2, Elias Pampalk2, Gerhard
Widmer2
1Universitat Pompeu Fabra, Barcelona, Spain
2Austrian Research Institute for AI, Vienna, Austria
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| 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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