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OFAI-TR-2002-23 ( 78kB g-zipped PostScript file,  131kB PDF file)

Playing Mozart by Analogy: Learning Multi-level Timing and Dynamics Strategies

Gerhard Widmer, Asmir Tobudic

The paper describes basic research in the area of machine learning and musical expression. A first step towards automatic induction of multi-level models of expressive performance (currently only tempo and dynamics) from real performances by skilled pianists is presented. The goal is to learn to apply sensible tempo and dynamics `shapes' at various levels of the hierarchical musical phrase structure. We propose a general method for decomposing given expression curves into elementary shapes at different levels, and for separating phrase-level expression patterns from local, note-level ones. We then present a hybrid learning system that learns to predict, via two different learning algorithms, both note-level and phrase-level expressive patterns, and combines these predictions into complex composite expression curves for new pieces. Experimental results indicate that the approach is generally viable; however, we also discuss a number of severe limitations that still need to be overcome in order to arrive at truly musical machine-generated performances.

Keywords: Machine Learning, Music Performance,

Citation: Widmer G., Tobudic A.: Playing Mozart by Analogy: Learning Multi-level Timing and Dynamics Strategies. Technical Report, Österreichisches Forschungsinstitut für Artificial Intelligence, Wien, TR-2002-23, 2002