Relational IBL in Music with a New Structural Similarity Measure
Identifieur interne : 001F10 ( Main/Exploration ); précédent : 001F09; suivant : 001F11Relational IBL in Music with a New Structural Similarity Measure
Auteurs : Asmir Tobudic [Autriche] ; Gerhard Widmer [Autriche]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2003.
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Abstract
Abstract: It is well known that many hard tasks considered in machine learning and data mining can be solved in an rather simple and robust way with an instance- and distance-based approach. In this paper we present another difficult task: learning, from large numbers of performances by concert pianists, to play music expressively. We model the problem as a multi-level decomposition and prediction task. Motivated by structural characteristics of such a task, we propose a new relational distance measure that is a rather straightforward combination of two existing measures. Empirical evaluation shows that our approach is in general viable and our algorithm, named DISTALL, is indeed able to produce musically interesting results. The experiments also provide evidence of the success of ILP in a complex domain such as music performance: it is shown that our instance-based learner operating on structured, relational data outperforms a propositional k-NN algorithm.
Url:
DOI: 10.1007/978-3-540-39917-9_24
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