Maximum a Posteriori ICA: Applying Prior Knowledge to the Separation of Acoustic Sources

Graham Taylor, Michael Seltzer, and Alex Acero

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.

Details

Publication typeInproceedings
Published inProc. of the Int. Conf. on Acoustics, Speech, and Signal Processing
PublisherInstitute of Electrical and Electronics Engineers, Inc.
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