A bayesian approach to time-frequency based blind source separation

In this paper we propose a bayesian approach for time-frequency (t-f) based source separation. We propose it Gibbs sampler, a standard Markov Chain Monte Carlo (MCMC) simulation method, to sample from the mixing, matrix, (he Source t-f coefficients and the input noise variance. under two models for the Sources. In the first one the t-f-coefficients of the sources are assumed i.i.d, while a frequency dependent modeling of the coefficients is proposed in the second one, which provides improved interference and noise rejection. Audio results are presented over several time resolutions of the t-f-transform.