Relational IBL in classical music

11 years 5 months ago
Relational IBL in classical music
It is well known that many hard tasks considered in machine learning and data mining can be solved in a rather simple and robust way with an instanceand distance-based approach. In this work we present another difficult task: learning, from large numbers of complex performances by concert pianists, to play music expressively. We model the problem as a multi-level decomposition and prediction task. We show that this is a fundamentally relational learning problem and propose a new similarity measure for structured objects, which is built into a relational instance-based learning algorithm named DISTALL. Experiments with data derived from a substantial number of Mozart piano sonata recordings by a skilled concert pianist demonstrate that the approach is viable. We show that the instance-based learner operating on structured, relational data outperforms a propositional k-NN algorithm. In qualitative terms, some of the piano performances produced by DISTALL after learning from the human art...
Asmir Tobudic, Gerhard Widmer
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2006
Where ML
Authors Asmir Tobudic, Gerhard Widmer
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