Abstract
The research presented in this paper focuses on global tempo transformations of monophonic audio recordings of saxophone jazz performances. We are investigating the problem of how a performance played at a particular tempo can be rendered automatically at another tempo, while preserving naturally sounding expressivity. Or, differently stated, how does expressiveness change with global tempo. Changing the tempo of a given melody is a problem that cannot be reduced to just applying a uniform transformation to all the notes of a musical piece. The expressive resources for emphasizing the musical structure of the melody and the affective content differ depending on the performance tempo. We present a case-based reasoning system called TempoExpress for addressing this problem, and describe the experimental results obtained with our approach. © Springer Science + Business Media, LLC 2006.
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Grachten, M., Arcos, J. L., & De Mántaras, R. L. (2006). A case based approach to expressivity-aware tempo transformation. Machine Learning, 65(2–3), 411–437. https://doi.org/10.1007/s10994-006-9025-9
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