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Pitch-Related Identification of Instruments in Classical Music Recordings

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New Frontiers in Mining Complex Patterns (NFMCP 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8983))

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Abstract

Identification of particular voices in polyphonic and polytimbral music is a task often performed by musicians in their everyday life. However, the automation of this task is very challenging, because of high complexity of audio data. Usually additional information is supplied, and the results are far from satisfactory. In this paper, we focus on classical music recordings, without requiring the user to submit additional information. Our goal is to identify musical instruments playing in short audio frames of polyphonic recordings of classical music. Additionally, we extract pitches (or pitch ranges) which combined with instrument information can be used in score-following and audio alignment, see e.g. [9, 20], or in works towards automatic score extraction, which are a motivation behind this work. Also, since instrument timbre changes with pitch, separate classifiers are trained for various pitch ranges for each instrument. Four instruments are investigated, representing stringed and wind instruments. The influence of adding harmonic (pitch-based) features to the feature set on the results is also investigated. Random forests are applied as a classification tool, and the results are presented and discussed.

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Acknowledgments

This work was partially supported by the Research Center of PJAIT, supported by the Ministry of Science and Higher Education in Poland.

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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 Academy 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. ICAR-CNR, Rende, Italy

    Elio Masciari

  6. University of North Carolina, Charlotte, USA and Warsaw University of Technology, Warsaw, Poland

    Zbigniew W. Ras

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Kubera, E., Wieczorkowska, A.A. (2015). Pitch-Related Identification of Instruments in Classical Music Recordings. In: Appice, A., Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2014. Lecture Notes in Computer Science(), vol 8983. Springer, Cham. https://doi.org/10.1007/978-3-319-17876-9_13

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  • DOI: https://doi.org/10.1007/978-3-319-17876-9_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-17875-2

  • Online ISBN: 978-3-319-17876-9

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