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OFAI-TR-2004-07 ( 101kB g-zipped PostScript file,  87kB PDF file)

Learning to Play Mozart : Recent Improvements

Asmir Tobudic, Gerhard Widmer

This paper describes basic research on the crossroads between machine learning and musicology. Starting from a system which is able to automatically induce multi-level tempo and dynamics models of expressive performance from a large corpus of real performances by skilled pianists, we discuss several of its shortcomings and present improvements and their empirical evaluation. In particular, we show that in a such complex domain as a concert-class musical performance, one can treat the training data as noisy. Applying a standard machine learning technique for noise handling indeed significantly improve the results. We also discuss the major drawback of standard propositional k nearest neighbor algorithm in case of learning mutually dependent concepts on different levels of resolution and present our solution to these problems by introducing a new relational instance-based learning algorithm. It turns out that it is indeed able to overcome some of the weaknesses of its propositional counterpart.

Citation: Tobudic A., Widmer G.: Learning to Play Mozart : Recent Improvements. Technical Report, Österreichisches Forschungsinstitut für Artificial Intelligence, Wien, TR-2004-07, 2004