Tracking Rests and Tempo Changes: Improved Score Following with Particle Filters

Description

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.

Period	14 Aug 2013
Event title	International Computer Music Conference (ICMC)
Event type	Conference
Location	AustraliaShow on map