BAYESIAN SEPARATION OF DISCRETE SOURCES VIA GIBBS SAMPLING
Stephane Senecal and Pierre-Olivier Amblard
e-mail: Stephane.Senecal@inpg.fr, Bidou.Amblard@inpg.fr
Source separation, one of the most recent domain of sig-
nal processing, consists in recovering signals mixed by an
unknown transmission channel. Many approaches manage
the separation using dierent techniques such as likelihood
and information theory (,) or high order statistics ().
This paper proposes a Bayesian approach to the problem of
an instantaneous linear mixing, considering the sources are
discrete. The powerful Gibbs sampling algorithm enables
to recover binary sources, but also the mixing coe∆cients
and noise levels with e∆ciency.