Tracking rests and Tempo changes: Improved Score following with Particle filters.
Filip Korzeniowski, Florian Krebs, Andreas Arzt, Gerhard Widmer · University of Michigan Library Repository · 2013
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.