Musical beat tracking via Kalman filtering and noisy measurements selection
We study the problem of automatic musical beat tracking from acoustic data, i.e., finding locations of beats of a music piece by computers on-the-fly, in this work. An on-line musical beat tracking algorithm based on Kalman filtering (KF) with an enhanced probability data association (EPDA) method is proposed. The beat tracking algorithm is built upon a linear dynamic model of beat progression, to which the Kalman filtering technique can be conveniently applied. The beat tracking performance can be seriously degraded by noisy measurements in the Kalman filtering process. Three methods are presented for noisy measurements selection. They are the local maximum (LM) method, the probabilistic data association (PDA) method and the enhanced PDA (EPDA) method. We see that the performance of EPDA outperforms that of LM and PDA significantly.