Abstract
An application of relational instance-based learning
to the complex task of expressive music performance
is presented. We investigate to what extent
a machine can automatically build 'expressive profiles' of famous pianists using only minimal
performance information extracted from audio
CD recordings by pianists and the printed
score of the played music. It turns out that the
machine-generated expressive performances on unseen
pieces are substantially closer to the real performances
of the 'trainer' pianist than those of all
others. Two other interesting applications of the
work are discussed: recognizing pianists from their
style of playing, and automatic style replication.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI'05), Edinburgh, Scotland |
| Number of pages | 6 |
| Publication status | Published - 2005 |
Fields of science
- 102 Computer Sciences
- 102001 Artificial intelligence
- 102003 Image processing
- 102015 Information systems
- 202002 Audiovisual media