Discovering Motifs with Variants in Music Databases View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017-10-04

AUTHORS

Riyadh Benammar , Christine Largeron , Véronique Eglin , Myléne Pardoen

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. More... »

PAGES

14-26

References to SciGraph publications

  • 2007-04. Constraint-based sequential pattern mining: the pattern-growth methods in JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
  • 2001-01. SPADE: An Efficient Algorithm for Mining Frequent Sequences in MACHINE LEARNING
  • 2016. The SPMF Open-Source Data Mining Library Version 2 in MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES
  • 2014. Fast Vertical Mining of Sequential Patterns Using Co-occurrence Information in ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING
  • 1997-09. Discovery of Frequent Episodes in Event Sequences in DATA MINING AND KNOWLEDGE DISCOVERY
  • 2011-02. Mining transposed motifs in music in JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
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    http://scigraph.springernature.com/pub.10.1007/978-3-319-68765-0_2

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    DIMENSIONS

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    143 schema:name Institut des Sciences de l’Homme (FRE 3768), 14 Avenue Berthelot, 69363, Lyon cedex 07, France
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    146 schema:name CNRS INSA-Lyon, LIRIS, UMR5205, 69621, Lyon, France
    147 Université De Lyon, Lyon, France
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