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OFAI-TR-2004-09 ( 180kB g-zipped PostScript file,  197kB PDF file)

Case-based Relational Learning of Expressive Phrasing in Classical Music

Asmir Tobudic, Gerhard Widmer

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.

Keywords: relational case-based learning, case-based reasoning, music

Citation: Tobudic A., Widmer G.: Case-based Relational Learning of Expressive Phrasing in Classical Music. Technical Report, Österreichisches Forschungsinstitut für Artificial Intelligence, Wien, TR-2004-09, 2004