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Machine Learning

, Volume 64, Issue 1–3, pp 5–24 | Cite as

Relational IBL in classical music

  • Asmir Tobudic
  • Gerhard WidmerEmail author
Article

Abstract

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 instance- and 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 artist are of substantial musical quality; one even won a prize in an international ‘computer music performance’ contest. The experiments thus provide evidence of the capabilities of ILP in a highly complex domain such as music.

Keywords

Relational instance-based learning Music 

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Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  1. 1.Austrian Research Institute for Artificial IntelligenceVienna
  2. 2.Department of Computational PerceptionJohannes Kepler University Linz, and Austrian Research Institute for Artificial IntelligenceVienna

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