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Challenges at the Interface of Data Analysis, Computer Science, and Optimization pp 393–400Cite as

Applying Multiple Kernel Learning to Automatic Genre Classification

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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.

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Notes

  1. 1.

    Available online: http://www.shogun-toolbox.org

  2. 2.

    Available online: http://ismir2004.ismir.net/genre_contest/index.htm

  3. 3.

    see http://www.globalmusic2one.net

  4. 4.

    see http://www.songs2see.net

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Acknowledgements

This work has been partly supported by the German research project GlobalMusic2One Footnote 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 Footnote 4, enabling transnational cooperation between Thuringian companies and their partners from other European regions.

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Authors and Affiliations

  1. Fraunhofer Institute for Digital Media Technology, Ehrenbergstr. 31, 98693, Ilmenau, Germany

    Hanna Lukashevich

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  1. Hanna Lukashevich
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Correspondence to Hanna Lukashevich .

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Editors and Affiliations

  1. Fak. Wirtschaftswissenschaften, Inst. Entscheidungstheorieund, Universität Karlsruhe (TH), Kaiserstr. 12, Karlsruhe, 76128, Germany

    Wolfgang A. Gaul

  2. Insitute for Information Systems, and Management (IISM), Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, Karlsruhe, 76131, Baden-Württemberg, Germany

    Andreas Geyer-Schulz

  3. , Information Systems, University ofHildesheim, Marienburger Platz 22, Hildesheim, 31141, Germany

    Lars Schmidt-Thieme

  4. Institute for Information Systems, and Management (IISM), Karlsruhe Institute of Technology (KIT), Kaiserstraße 12, Karlsruhe, 76128, Germany

    Jonas Kunze

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Lukashevich, H. (2012). Applying Multiple Kernel Learning to Automatic Genre Classification. In: Gaul, W., Geyer-Schulz, A., Schmidt-Thieme, L., Kunze, J. (eds) Challenges at the Interface of Data Analysis, Computer Science, and Optimization. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24466-7_40

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