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Stochastic Feature Selection in Support Vector Machine Based Instrument Recognition

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KI 2009: Advances in Artificial Intelligence (KI 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5803))

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Abstract

Automatic instrument recognition is an important task in musical applications. In this paper we concentrate on the recognition of electronic drum sounds from a large commercially available drum sound library. The recognition task can be formulated as classification problem. Each sample is described by one hundred temporal and spectral features. Support Vector Machines turn out to be an excellent choice for this classification task. Furthermore, we concentrate on the stochastic optimization of a feature subset using evolution strategies and compare the results to the classifier that has been trained on the complete feature set.

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References

  1. Beyer, H.-G., Schwefel, H.-P.: Evolution strategies - A comprehensive introduction. Natural Computing 1, 3–52 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  2. Bishop, C.M.: Springer (August 2006)

    Google Scholar 

  3. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001)

    Google Scholar 

  4. Friedrichs, F., Igel, C.: Evolutionary tuning of multiple SVM parameters. Neurocomputing 64, 107–117 (2005)

    Article  Google Scholar 

  5. Gillet, O., Richard, G.: Transcription and separation of drum signals from polyphonic music 16(3), 529–540 (2008)

    Google Scholar 

  6. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)

    MATH  Google Scholar 

  7. Herrera-Boyer, P., Peeters, G., Dubnov, S.: Automatic classification of musical instrument sounds. New Music Research 32(1), 13–21 (2003)

    Google Scholar 

  8. Kramer, O., Stein, B., Wall, J.: Ai and music: Toward a taxonomy of problem classes. In: ECAI, pp. 695–696 (2006)

    Google Scholar 

  9. Mierswa, I.: Evolutionary learning with kernels: A generic solution for large margin problems (2006)

    Google Scholar 

  10. Peeters, G.: A large set of audio features for sound description (similarity and classification) in the CUIDADO project. Tech. rep., IRCAM (2004)

    Google Scholar 

  11. Stoean, R., Dumitrescu, D., Preuss, M., Stoean, C.: Evolutionary support vector regression machines. In: SYNASC, pp. 330–335 (2006)

    Google Scholar 

  12. Stoean, R., Preuss, M., Stoean, C., Dumitrescu, D.: Concerning the potential of evolutionary support vector machines. In: IEEE Congress on Evolutionary Computation, pp. 1436–1443 (2007)

    Google Scholar 

  13. Van Steelant, D., Tanghe, K., Degroeve, S., De Baets, B., Leman, M., Martens, J.: Support Vector Machines for Bass and Snare Drum Recognition (2005)

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Technische Universität Dortmund, 44227, Dortmund, Germany

    Oliver Kramer & Tobias Hein

Authors
  1. Oliver Kramer
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  2. Tobias Hein
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Editors and Affiliations

  1. GET Lab, University of Paderborn, Pohlweg 47-49, 33098, Paderborn, Germany

    Bärbel Mertsching , Marcus Hund  & Zaheer Aziz ,  & 

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© 2009 Springer-Verlag Berlin Heidelberg

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Kramer, O., Hein, T. (2009). Stochastic Feature Selection in Support Vector Machine Based Instrument Recognition. In: Mertsching, B., Hund, M., Aziz, Z. (eds) KI 2009: Advances in Artificial Intelligence. KI 2009. Lecture Notes in Computer Science(), vol 5803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04617-9_91

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  • DOI: https://doi.org/10.1007/978-3-642-04617-9_91

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04616-2

  • Online ISBN: 978-3-642-04617-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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