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Applying Multiple Kernel Learning to Automatic Genre Classification

  • Hanna Lukashevich
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

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.

Keywords

Support Vector Machine Radial Basis Function Kernel Audio Feature Multiple Kernel Learn Genre Classification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work has been partly supported by the German research project GlobalMusic2One 3 funded by the Federal Ministry of Education and Research (BMBF-FKZ: 01/S08039B). Additionally, the Thuringian Ministry of Economy, Employment and Technology supported this research by granting funds of the European Fund for Regional Development to the project Songs2See 4, enabling transnational cooperation between Thuringian companies and their partners from other European regions.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Fraunhofer Institute for Digital Media TechnologyIlmenauGermany

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