Abstract
In this paper we present a score following system based
on a Dynamic Bayesian Network, using particle filtering
as inference method. The proposed model sets itself apart
from existing approaches by including two new extensions:
A multi-level tempo model to improve alignment quality
of performances with challenging tempo changes, and
an extension to reflect different expressive characteristics
of notated rests.
Both extensions are evaluated against a dataset of classical
piano music. As the results show, the extensions improve
both the accuracy and the robustness of the algorithm.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the International Computer Music Conference (ICMC) |
| Number of pages | 7 |
| Publication status | Published - 2013 |
Fields of science
- 102 Computer Sciences
- 102001 Artificial intelligence
- 102003 Image processing
JKU Focus areas
- Computation in Informatics and Mathematics
- Engineering and Natural Sciences (in general)