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Discovering Motifs with Variants in Music Databases

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Advances in Intelligent Data Analysis XVI (IDA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10584))

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Abstract

Music score analysis is an ongoing issue for musicologists. Discovering frequent musical motifs with variants is needed in order to make critical study of music scores and investigate compositions styles. We introduce a mining algorithm, called CSMA for Constrained String Mining Algorithm, to meet this need considering symbol-based representation of music scores. This algorithm, through motif length and maximal gap constraints, is able to find identical motifs present in a single string or a set of strings. It is embedded into a complete data mining process aiming at finding variants of musical motif. Experiments, carried out on several datasets, showed that CSMA is efficient as string mining algorithm applied on one string or a set of strings.

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Acknowledgement

The funding for this project was provided by a grant from la région Rhone Alpes.

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Authors and Affiliations

  1. Université De Lyon, Lyon, France

    Riyadh Benammar, Christine Largeron & Véronique Eglin

  2. CNRS INSA-Lyon, LIRIS, UMR5205, 69621, Lyon, France

    Riyadh Benammar & Véronique Eglin

  3. UJM-Saint-Etienne, CNRS, Institut d’Optique Graduate School, Laboratoire Hubert Curien UMR 5516, 42023, Saint-Etienne, France

    Christine Largeron

  4. Institut des Sciences de l’Homme (FRE 3768), 14 Avenue Berthelot, 69363, Lyon cedex 07, France

    Myléne Pardoen

Authors
  1. Riyadh Benammar
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  2. Christine Largeron
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  3. Véronique Eglin
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  4. Myléne Pardoen
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Corresponding author

Correspondence to Riyadh Benammar .

Editor information

Editors and Affiliations

  1. Imperial College London, London, United Kingdom

    Niall Adams

  2. Brunel University London, Uxbridge, United Kingdom

    Allan Tucker

  3. Birkbeck, University of London, London, United Kingdom

    David Weston

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Benammar, R., Largeron, C., Eglin, V., Pardoen, M. (2017). Discovering Motifs with Variants in Music Databases. In: Adams, N., Tucker, A., Weston, D. (eds) Advances in Intelligent Data Analysis XVI. IDA 2017. Lecture Notes in Computer Science(), vol 10584. Springer, Cham. https://doi.org/10.1007/978-3-319-68765-0_2

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  • DOI: https://doi.org/10.1007/978-3-319-68765-0_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68764-3

  • Online ISBN: 978-3-319-68765-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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