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Multi-label Ferns for Efficient Recognition of Musical Instruments in Recordings

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Foundations of Intelligent Systems (ISMIS 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8502))

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Abstract

In this paper we introduce multi-label ferns, and apply this technique for automatic classification of musical instruments in audio recordings. We compare the performance of our proposed method to a set of binary random ferns, using jazz recordings as input data. Our main result is obtaining much faster classification and higher F-score. We also achieve substantial reduction of the model size.

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Author information

Authors and Affiliations

  1. Interdisciplinary Centre for Mathematical and Computational Modelling (ICM), University of Warsaw, Pawińskiego 5A, 02-106, Warsaw, Poland

    Miron B. Kursa

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

    Alicja A. Wieczorkowska

Authors
  1. Miron B. Kursa
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  2. Alicja A. Wieczorkowska
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Editor information

Editors and Affiliations

  1. Research Group PLIS: Programming, Logic and Intelligent Systems Dept. of Communication, Business and Information Technologies, Roskilde University, Denmark

    Troels Andreasen  & Henning Christiansen  & 

  2. Department of Computer Science and Artificial Intelligence, CITIC, University of Granada, 18071, Granada, Spain

    Juan-Carlos Cubero

  3. University of North Carolina, , , 9201 University City Blvd, Charlotte, NC 28223 USA, and Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland

    Zbigniew W. Raś

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Kursa, M.B., Wieczorkowska, A.A. (2014). Multi-label Ferns for Efficient Recognition of Musical Instruments in Recordings. In: Andreasen, T., Christiansen, H., Cubero, JC., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2014. Lecture Notes in Computer Science(), vol 8502. Springer, Cham. https://doi.org/10.1007/978-3-319-08326-1_22

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  • DOI: https://doi.org/10.1007/978-3-319-08326-1_22

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08325-4

  • Online ISBN: 978-3-319-08326-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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