A case based approach to expressivity-aware tempo transformation

Authors:
Maarten Grachten;Josep-Lluís Arcos;Ramon López Mántaras
Affiliations:
IIIA, Artificial Intelligence Research Institute, CSIC, Spanish Council for Scientific Research, Bellaterra, Spain 08193;IIIA, Artificial Intelligence Research Institute, CSIC, Spanish Council for Scientific Research, Bellaterra, Spain 08193;IIIA, Artificial Intelligence Research Institute, CSIC, Spanish Council for Scientific Research, Bellaterra, Spain 08193
Venue:
Machine Learning
Year:
2006

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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.