Blind Source Separation Using Mixtures of Alpha-Stable Distributions

We propose a new blind source separation algorithm based on mixtures of $\alpha$-stable distributions. Complex symmetric $\alpha$-stable distributions have been recently showed to better model audio signals in the time-frequency domain than classical Gaussian distributions thanks to their larger dynamic range. However, inference with these models is notoriously hard to perform because their probability density functions do not have a closed-form expression in general. Here, we introduce a novel method for estimating mixtures of $\alpha$-stable distributions based on characteristic function matching. We apply this to the blind estimation of binary masks in individual frequency bands from multichannel convolutive audio mixtures. We show that the proposed method yields better separation performance than Gaussian-based binary-masking methods.