  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 of 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 mixture 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 mixes. We show that the proposed method yields
better separation performance than Gaussian-based binary-masking methods.
