MULTICHANNEL AUDIO SOURCE SEPARATION WITH PROBABILISTIC REVERBERATION MODELING

In this paper we show that considering early contributions of mixing filters through a probabilistic prior can help blind source separation in reverberant recording conditions. By modeling mixing filters as the direct path plus R-1 reflections, we represent the propagation from a source to a mixture channel as an autoregressive process of order R in the frequency domain. This model is used as a prior to derive a Maximum A Posteriori (MAP) estimation of the mixing filters using the Expectation-Maximization (EM) algorithm. Experimental results over reverberant synthetic mixtures and live recordings show that MAP estimation with this prior provides better separation results than a Maximum Likelihood (ML) estimation.