Tracking and data association
Stochastic differential equations (3rd ed.): an introduction with applications
Stochastic differential equations (3rd ed.): an introduction with applications
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Monte Carlo methods for tempo tracking and rhythm quantization
Journal of Artificial Intelligence Research
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Particle filters for state estimation of jump Markov linear systems
IEEE Transactions on Signal Processing
| Hi-index | 0.00 |
This paper explores the use of particle filters for beat tracking in musical audio examples. The aim is to estimate the time-varying tempo process and to find the time locations of beats, as defined by human perception. Two alternative algorithms are presented, one which performs Rao-Blackwellisation to produce an almost deterministic formulation while the second is a formulation which models tempo as a Brownian motion process. The algorithms have been tested on a large and varied database of examples and results are comparable with the current state of the art. The deterministic algorithm gives the better performance of the two algorithms.