Abstract

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.