|Title||Tracking Rests and Tempo Changes: Improved Score Following with Particle Filters|
|Publication Type||Conference Paper|
|Year of Publication||2013|
|Authors||Korzeniowski, F, Krebs, F, Arzt, A, Widmer, G|
|Conference Name||Proceedings of the International Computer Music Conference (ICMC)|
|Conference Location||Perth, Australia|
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.