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.

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In: Proc. of the Int. Conf. on Acoustics, Speech, and Signal Processing

Publisher: Institute of Electrical and Electronics Engineers, Inc.
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