Modeling Expressive Music Performance in Jazz

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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 recordings of Jazz standards 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.