  Source separation is one of the signal processing's main emerging domain.
Many techniques such as maximum likelihood (ML), Infomax, cumulant matching,
estimating function, etc. have been used to address this difficult problem.
Unfortunately, up to now, many of these methods could not account completely
for noise on the data, for different number of sources and sensors, for lack of
spatial independence and for time correlation of the sources. Recently, the
Bayesian approach has been used to push farther these limitations of the
conventional methods. This paper proposes a unifying approach to source
separation based on the Bayesian estimation. We first show that this approach
gives the possibility to explain easily the major known techniques in sources
separation as special cases. Then we propose new methods based on maximum a
posteriori (MAP) estimation, either to estimate directly the sources, or the
mixing matrices or even both.
