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Understanding expressive transformations in saxophone jazz performances using inductive machine learning

Title Understanding expressive transformations in saxophone jazz performances using inductive machine learning
Publication Type Conference Paper
Year of Publication 2004
Conference Name Sound and Music Computing Conference
Authors Ramirez, R. , Hazan A. , Gómez E. , & Maestre E.
Abstract In this paper, we describe an approach to learning expressive performance rules from monophonic Jazz standards recordings 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 to this representation in order to induce rules of expressive music performance. It turns out that some of the induced rules represent extremely simple principles which are surprisingly general.
preprint/postprint document files/publications/smc04-RamirezHazanGomezMaestre.pdf