Applying multiple kernel learning to automatic genre classification

Abstract

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. © 2012 Springer-Verlag Berlin Heidelberg.