Discovering expressive transformation rules from saxophone jazz performances

If-then rules are one of the most expressive and intuitive knowledge representations and their application to represent musical knowledge raises particularly interesting questions. In this paper, we describe an approach to learning expressive performance rules from monophonic recordings of jazz standards by a skilled saxophonist. We have first developed a melodic transcription system which extracts a set of acoustic features from the recordings producing a melodic representation of the expressive performance played by the musician. We apply machine learning techniques, namely inductive logic programming, to this representation in order to induce first order logic rules of expressive music performance.