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Statistical Music Modeling Aimed at Identification and Alignment

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Advances in Music Information Retrieval

Part of the book series: Studies in Computational Intelligence ((SCI,volume 274))

Abstract

This paper describes a methodology for the statistical modeling of music works. Starting from either the representation of the symbolic score or the audio recording of a performance, a hidden Markov model is built to represent the corresponding music work. The model can be used to identify unknown recordings and to align them with the corresponding score. Experimental evaluation using a collection of classical music recordings showed that this approach is effective in terms of both identification and alignment. The methodology can be exploited as the core component for a set of tools aimed at accessing and actively listening to a music collection.

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Author information

Authors and Affiliations

  1. Department of Information Engineering, University of Padova,  

    Riccardo Miotto, Nicola Montecchio & Nicola Orio

Authors
  1. Riccardo Miotto
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  2. Nicola Montecchio
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  3. Nicola Orio
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Editor information

Editors and Affiliations

  1. University of North Carolina, Charlotte, NC, USA

    Zbigniew W. Raś

  2. Polish-Japanese Institute of IT, Warsaw, Poland

    Alicja A. Wieczorkowska

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Miotto, R., Montecchio, N., Orio, N. (2010). Statistical Music Modeling Aimed at Identification and Alignment. In: Raś, Z.W., Wieczorkowska, A.A. (eds) Advances in Music Information Retrieval. Studies in Computational Intelligence, vol 274. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11674-2_9

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  • DOI: https://doi.org/10.1007/978-3-642-11674-2_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-11673-5

  • Online ISBN: 978-3-642-11674-2

  • eBook Packages: EngineeringEngineering (R0)

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