- Title
- A class centric feature and classifier ensemble selection approach for music genre classification
- Creator
- Ariyaratne, Hasitha Bimsara; Zhang, Dengsheng; Lu, Guojun
- Date
- 2012
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/161404
- Identifier
- vital:12447
- Identifier
-
https://doi.org/10.1007/978-3-642-34166-3_73
- 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.
- Publisher
- Springer
- Relation
- Joint IAPR International Workshop SSPR & SPR 2012 p. 666-674
- Rights
- © Springer-Verlag Berlin Heidelberg 2012
- Rights
- This metadata is freely available under a CCO license
- Subject
- Music retrieval; Feature selection; Classifier ensemble; Music genre classification
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