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...

• Type: Journal
• Year: 2006
• Where: ML
• Authors: Asmir Tobudic, Gerhard Widmer
• Keywords: Concert Pianist, Instanceand Distance-based Approach, Machine Learning, ML 2006, Relational Learning Problem