Abstract
We propose novel machine learning methods for exploring the domain of music perfor-
mance praxis. Based on simple measurements of timing and intensity in 12 recordings
of a Schubert piano piece, short performance sequences are fed into a SOM algorithm in
order to calculate performance archetypes. The archetypes are labeled with letters and
approximate string matching done by an evolutionary algorithm is applied to find simi-
larities in the performances represented by these letters. We present a way of measuring
each pianists habit of playing similar phrases in similar ways and propose a ranking
of the performers based on that. Finally, an experiment revealing common expression
patterns is briefly described.
Keywords: Self Organizing Map, Evolutionary Algorithm, Approximate String Matching,
Expressive Music Performance
| Original language | English |
|---|---|
| Pages (from-to) | 495-514 |
| Number of pages | 20 |
| Journal | International Journal of Artificial Intelligence Tools |
| Volume | 15 |
| Issue number | 4 |
| Publication status | Published - 2006 |
Fields of science
- 102 Computer Sciences
- 102001 Artificial intelligence
- 102003 Image processing
- 102015 Information systems
- 202002 Audiovisual media