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OFAI-TR-2000-19 ( 49kB g-zipped PostScript file,  125kB PDF file)

Large-scale Induction of Expressive Performance Rules: First Quantitative Results

Gerhard Widmer

The paper presents first 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 find some structure in the data. It also turns out that meter and global tempo have a strong influence on expression patterns. Finally, we briefly describe an experiment that demonstrates how machine learning can be used to study and possibly resolve some specialized questions.

Keywords: Machine Learning, Music, Expressive Music Performance

Citation: Widmer, G. (2000). Large-scale Induction of Expressive Performance Rules: First Quantitative Results. In Proceedings of the International Computer Music Conference (ICMC'2000). San Francisco, CA: International Computer Music Association.