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Applying multiple kernel learning to automatic genre classification

: Lukashevich, Hanna


Gaul, Wolfgang ; Gesellschaft für Klassifikation:
Challenges at the interface of data analysis, computer science, and optimization : Proceedings of the 34th annual conference of the Gesellschaft für Klassifikation e. V., Karlsruhe, July 21 - 23, 2010
Berlin: Springer, 2012 (Studies in classification, data analysis, and knowledge organization)
ISBN: 978-3-642-24465-0 (Print)
ISBN: 978-3-642-24466-7 (Online)
ISBN: 3-642-24465-3
Gesellschaft für Klassifikation (GfKl Annual Conference) <34, 2010, Karlsruhe>
Fraunhofer IDMT ()
music classification; kernel learning

In this paper we demonstrate the advantages of multiple-kernel learning in the application to music genre classification. Multiple-kernel learning provides the possibility to adaptively tune the kernel settings to each group of features independently. Our experiments show the improvement of classification performance in comparison to the conventional support vector machine classifier.