@conference {90, title = {Tracking Rests and Tempo Changes: Improved Score Following with Particle Filters}, booktitle = {Proceedings of the International Computer Music Conference (ICMC)}, year = {2013}, address = {Perth, Australia}, 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.

}, author = {Korzeniowski, Filip and Krebs, Florian and Andreas Arzt and Widmer, Gerhard} }