Case-Based Relational Learning of Expressive Phrasing in Classical Music

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.

• Type: Conference
• Year: 2004
• Where: EWCBR
• Authors: Asmir Tobudic, Gerhard Widmer
• Keywords: Artificial Intelligence, Case-based Learner Distall, Distance Measure, EWCBR 2004, Music Task