Abstract
In this paper, we describe an approach to learning expressive performance rules from monophonic Jazz standards recordings by a skilled saxophonist. We use 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 genetic algorithms to this representation in order to induce rules of expressive music performance. The rules collected during different runs of our system are of musical interest and have a good prediction accuracy.
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Ramirez, R., Hazan, A. (2005). Understanding Expressive Music Performance Using Genetic Algorithms. In: Rothlauf, F., et al. Applications of Evolutionary Computing. EvoWorkshops 2005. Lecture Notes in Computer Science, vol 3449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32003-6_51
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DOI: https://doi.org/10.1007/978-3-540-32003-6_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25396-9
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