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Mining Audio Data for Multiple Instrument Recognition in Classical Music

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New Frontiers in Mining Complex Patterns (NFMCP 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8399))

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Abstract

This paper addresses the problem of identification of multiple musical instruments in polyphonic recordings of classical music. A set of binary random forests was used as a classifier, and each random forest was trained to recognize the target class of sounds. Training data were prepared in two versions, one based on single sounds and their mixes, and the other containing also sound frames taken from classical music recordings. The experiments on identification of multiple instrument sounds in recordings are presented, and their results are discussed in this paper.

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Acknowledgments

This project was partially supported by the Research Center of PJIIT, supported by the Polish Ministry of Science and Higher Education.

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Authors and Affiliations

  1. University of Life Sciences in Lublin, Akademicka 13, 20-950, Lublin, Poland

    Elżbieta Kubera

  2. Polish-Japanese Institute of Information Technology, Koszykowa 86, 02-008, Warsaw, Poland

    Alicja A. Wieczorkowska

Authors
  1. Elżbieta Kubera
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  2. Alicja A. Wieczorkowska
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Corresponding author

Correspondence to Elżbieta Kubera .

Editor information

Editors and Affiliations

  1. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Annalisa Appice

  2. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Michelangelo Ceci

  3. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Corrado Loglisci

  4. ICAR, CNR, Rende, Italy

    Giuseppe Manco

  5. Rende, Italy

    Elio Masciari

  6. Department of Computer Science, University of North Carolina, Charlotte, North Carolina, USA

    Zbigniew W. Ras

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Kubera, E., Wieczorkowska, A.A. (2014). Mining Audio Data for Multiple Instrument Recognition in Classical Music. In: Appice, A., Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2013. Lecture Notes in Computer Science(), vol 8399. Springer, Cham. https://doi.org/10.1007/978-3-319-08407-7_16

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  • DOI: https://doi.org/10.1007/978-3-319-08407-7_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08406-0

  • Online ISBN: 978-3-319-08407-7

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