Musical Instrument Classification

Automatic musical instrument classification is the first step in developing applications for:

  • Automatic music transcription
  • Effective data organization and search
  • Multimedia databases annotation

 

Our Method

A Sound Description Toolbox for automatic feature extraction from audio files has been developed. Features covered are:

  • Sound description features used in general audio data (GAD) classification experiments
  • Spectral descriptors defined by the MPEG-7 audio standard are covered

 

A multitude of classifiers has been developed:

  • A novel supervised classifier based on non-negative matrix factorization (NMF) techniques

  • Multilayer perceptrons (MLPs)
  • Radial basis functions (RBF) networks
  • Support vector machines (SVMs)
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    Relevant Publications

    E. Benetos, M. Kotti, C. Kotropoulos, J. J. Burred, G. Eisenberg, M. Haller, and T. Sikora, "Comparison of subspace analysis-based and statistical model-based algorithms for musical instrument classification", in Proc. 2nd Workshop On Immersive Communication And Broadcast Systems, October 2005.

    E. Benetos, M. Kotti, and C. Kotropoulos, "Applying supervised classifiers based on non-negative matrix factorization to musical instrument classification", in Proc. IEEE Int. Conf. Multimedia & Expo (ICME 2006), Toronto, Ontario, Canada, 9-12 July, 2006.

    E. Benetos, C. Kotropoulos, T. Lidy, and A. Rauber, "Testing supervised classifiers based on non-negative matrix factorization to musical instrument classification", in Proc. 14th European Signal Processing Conf.,  September 2006.

     

    Research Projects

    PYTHAGORAS-II - "Efficient techniques for information organization, browsing, and retrieval in multimedia'', funded by the Greek Ministry of Education and the European Union

    MUSCLE - “Multimedia Understanding through Semantics, Computation and LEarning” (FP6-507752)

     

    © 2006