Large-scale Induction of Expressive Performance Rules: First Quantitative Results
Gerhard Widmer · 2000
The paper presents rst experimental results of a research project that aims at identifying basic principles of expressive music performance with the help of machine learning methods. Various learning algorithms were applied to a large collection of real performance data (recordings of 13 Mozart sonatas by a skilled pianist) in order to induce general categorical expression rules for tempo, dynamics, and articulation. Preliminary results show that the algorithms can indeed nd some structure in the data. It also turns out that meter and global tempo have a strong inuence on expression patterns. Finally, we briey describe an experiment that demonstrates how machine learning can be used to study and possibly resolve some specialized questions. 1 Introduction In this paper, we present rst quantitative results of a long-term research project that aims at identifying and studying basic principles of expressive music performance with the help of Articial Intelligence (in partic...