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.

}, author = {Graham Taylor and Michael Seltzer and Alex Acero}, booktitle = {Proc. of the Int. Conf. on Acoustics, Speech, and Signal Processing}, month = {April}, publisher = {Institute of Electrical and Electronics Engineers, Inc.}, title = {Maximum a Posteriori ICA: Applying Prior Knowledge to the Separation of Acoustic Sources}, url = {http://research.microsoft.com/apps/pubs/default.aspx?id=78405}, year = {2008}, }