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

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, notelevel 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.