MozartV1, Istex, Corpus, bibRecord, 000334

Case-Based Relational Learning of Expressive Phrasing in Classical Music

Identifieur interne : 000334 ( Istex/Corpus ); précédent : 000333; suivant : 000335

Case-Based Relational Learning of Expressive Phrasing in Classical Music

Auteurs : Asmir Tobudic ; Gerhard Widmer

Source :

Lecture Notes in Computer Science [ 0302-9743 ] ; 2004.

RBID : ISTEX:4F0C5C41468F5CAF39696A2E46C7578B8DF64975

English descriptors

KwdEn :
- case-based reasoning, music, relational case-based learning.

Abstract

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.

Url:

https://api.istex.fr/document/4F0C5C41468F5CAF39696A2E46C7578B8DF64975/fulltext/pdf

DOI: 10.1007/978-3-540-28631-8_31

Links to Exploration step

ISTEX:4F0C5C41468F5CAF39696A2E46C7578B8DF64975

Le document en format XML

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<abstract lang="en">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.</abstract>
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Case-Based Relational Learning of Expressive Phrasing in Classical Music

Case-Based Relational Learning of Expressive Phrasing in Classical Music

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