Induction of expressive music performance models

In this paper we describe a machine learning approach to one of the most challenging aspects of computer music: modeling the knowledge applied by a musician when performing a score in order to produce an expressive performance of a piece. We apply machine learning techniques to a set of monophonic Jazz standards recordings in order to induce both rules and a numeric model for expressive performance. We implement a tool for automatic expressive performance transformations of Jazz melodies using the induced knowledge.