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Inducing a generative expressive performance model using a sequential-covering genetic algorithm

In this paper, we describe an evolutionary approach to inducing a generative model of expressive music performance for Jazz saxophone. We begin with a collection of audio recordings of real Jazz saxophone performances from which we extract a symbolic representation of the musician's expressive performance. We then apply an evolutionary algorithm to the symbolic representation in order to obtain computational models for different aspects of expressive performance. Finally, we use these models to automatically synthesize performances with the expressiveness that characterizes the music generated by a professional saxophonist.