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Musical Instrument Separation Applied to Music Genre Classification

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Foundations of Intelligent Systems (ISMIS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9384))

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Abstract

This paper outlines first issues related to music genre classification and a short description of algorithms used for musical instrument separation. Also, the paper presents proposed optimization of the feature vectors used for music genre recognition. Then, the ability of decision algorithms to properly recognize music genres is discussed based on two databases. In addition, results are cited for another database with regard to the efficiency of the feature vector.

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Acknowledgments

The study was partially supported by the EU from the European Social Fund (UDA-POKL.04.01.01-00-106/09) and the project PBS1/B3/16/2012 financed by the Polish National Centre for R&D.

Author information

Authors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland

    Aldona Rosner

  2. Audio Acoustics Laboratory, Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, 80-233, Gdansk, Poland

    Bozena Kostek

Authors
  1. Aldona Rosner
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  2. Bozena Kostek
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Corresponding author

Correspondence to Aldona Rosner .

Editor information

Editors and Affiliations

  1. Computer Science, University of Bari, Bari, Italy

    Floriana Esposito

  2. Enssat, Lannion, France

    Olivier Pivert

  3. LISI-UFR d'Informatique, Université Claude Bernard Lyon 1, Villeurbanne Cedex, France

    Mohand-Said Hacid

  4. University of North Carolina, CHARLOTTE, North Carolina, USA

    Zbigniew W. Rás

  5. Dipartimento di Informatica, Università degli Studi di Bari, Bari, Italy

    Stefano Ferilli

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Rosner, A., Kostek, B. (2015). Musical Instrument Separation Applied to Music Genre Classification. In: Esposito, F., Pivert, O., Hacid, MS., Rás, Z., Ferilli, S. (eds) Foundations of Intelligent Systems. ISMIS 2015. Lecture Notes in Computer Science(), vol 9384. Springer, Cham. https://doi.org/10.1007/978-3-319-25252-0_45

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  • DOI: https://doi.org/10.1007/978-3-319-25252-0_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25251-3

  • Online ISBN: 978-3-319-25252-0

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