Case-Based Relational Learning of Expressive Phrasing in Classical Music View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2004

AUTHORS

Asmir Tobudic , Gerhard Widmer

ABSTRACT

An application of relational case-based learning to the task of expressive music performance is presented. We briefly recapitulate the relational case-based learner DISTALL and empirically show that DISTALL outperforms a straightforward propositional k-NN on the music task. A set distance measure based on maximal matching – incorporated in DISTALL – is discussed in more detail and especially the problem associated with its ‘penalty part’: the distance between a large and a small set is mainly determined by their difference in cardinality. We introduce a method for systematically varying the influence of the penalty on the overall distance measure and experimentally test different variants of it. Interestingly, it turns out that the variants with high influence of penalty clearly perform better than the others on our music task. More... »

PAGES

419-433

References to SciGraph publications

  • 2001-01. An Interactive Case-Based Reasoning Approach for Generating Expressive Music in APPLIED INTELLIGENCE
  • 1998. A framework for defining distances between first-order logic objects in INDUCTIVE LOGIC PROGRAMMING
  • 2003-06-18. Playing Mozart Phrase by Phrase in CASE-BASED REASONING RESEARCH AND DEVELOPMENT
  • 2003. Relational IBL in Music with a New Structural Similarity Measure in INDUCTIVE LOGIC PROGRAMMING
  • 2001-07. A polynomial time computable metric between point sets in ACTA INFORMATICA
  • Book

    TITLE

    Advances in Case-Based Reasoning

    ISBN

    978-3-540-22882-0
    978-3-540-28631-8

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-540-28631-8_31

    DOI

    http://dx.doi.org/10.1007/978-3-540-28631-8_31

    DIMENSIONS

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