  Blind source separation (BSS) is addressed, using a novel data-driven
approach, based on a well-established probabilistic model. The proposed method
is specifically designed for separation of multichannel audio mixtures. The
algorithm relies on spectral decomposition of the correlation matrix between
different time frames. The probabilistic model implies that the column space of
the correlation matrix is spanned by the probabilities of the various speakers
across time. The number of speakers is recovered by the eigenvalue decay, and
the eigenvectors form a simplex of the speakers' probabilities. Time frames
dominated by each of the speakers are identified exploiting convex geometry
tools on the recovered simplex. The mixing acoustic channels are estimated
utilizing the identified sets of frames, and a linear umixing is performed to
extract the individual speakers. The derived simplexes are visually
demonstrated for mixtures of 2, 3 and 4 speakers. We also conduct a
comprehensive experimental study, showing high separation capabilities in
various reverberation conditions.
