Tracking Rests and Tempo Changes: Improved Score Following with Particle Filters
Sprache des Titels:
Proceedings of the International Computer Music Conference (ICMC)
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.