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
Beyer, H.-G., Schwefel, H.-P.: Evolution strategies - A comprehensive introduction. Natural Computing 1, 3–52 (2002)
Bishop, C.M.: Springer (August 2006)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001)
Friedrichs, F., Igel, C.: Evolutionary tuning of multiple SVM parameters. Neurocomputing 64, 107–117 (2005)
Gillet, O., Richard, G.: Transcription and separation of drum signals from polyphonic music 16(3), 529–540 (2008)
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)
Herrera-Boyer, P., Peeters, G., Dubnov, S.: Automatic classification of musical instrument sounds. New Music Research 32(1), 13–21 (2003)
Kramer, O., Stein, B., Wall, J.: Ai and music: Toward a taxonomy of problem classes. In: ECAI, pp. 695–696 (2006)
Mierswa, I.: Evolutionary learning with kernels: A generic solution for large margin problems (2006)
Peeters, G.: A large set of audio features for sound description (similarity and classification) in the CUIDADO project. Tech. rep., IRCAM (2004)
Stoean, R., Dumitrescu, D., Preuss, M., Stoean, C.: Evolutionary support vector regression machines. In: SYNASC, pp. 330–335 (2006)
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)
Van Steelant, D., Tanghe, K., Degroeve, S., De Baets, B., Leman, M., Martens, J.: Support Vector Machines for Bass and Snare Drum Recognition (2005)
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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
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