Polyphonic pitch tracking using joint Bayesian estimation of multiple frame parameters

We present a novel approach to pitch estimation and note detection in polyphonic audio signals. We pose the problem in a Bayesian probabilistic framework, which allows us to incorporate prior knowledge about the nature of musical data into the model. We exploit the high correlation between model parameters in adjacent frames of data by explicitly modelling the frequency variation over time using latent variables. Parameters are estimated jointly across a number of adjacent frames to increase the robustness of the estimation against transient events. Individual frames of data are modelled as the sum of harmonic sinusoids. Parameter estimation is performed using Markov chain Monte Carlo (MCMC) methods.