Independent component analysis (ICA) for convolutive mixtures is
often applied in the frequency domain due to the desirable decoupling
into independent instantaneous mixtures per frequency bin.
This approach suffers from a well-known scaling and permutation
ambiguity. Existingmethods perform a computation-heavy and sometimes
unreliable phase of post-processing which typically makes use
of knowledge regarding the geometry of the sensors post-ICA. In this
paper, we propose a natural way to incorporate a priori knowledge of
the unmixing matrix in the form of a prior distribution. This softly
constrains ICA in a manner that avoids the permutation problem,
and also allows us to integrate information about the environment,
such as likely user configurations, into ICA using a unified statistical
framework. Maximum a priori ICA easily follows from the
maximum likelihood derivation of ICA. Its effectiveness is demonstrated
through a series of experiments on convolutive mixtures of
speech signals.