DocumentCode :
3411849
Title :
Maximum a posteriori ICA: Applying prior knowledge to the separation of acoustic sources
Author :
Taylor, Graham W. ; Seltzer, Michael L. ; Acero, Alex
Author_Institution :
Dept. of Comput. Sci., Univ. of Toronto, Toronto, ON
fYear :
2008
fDate :
March 31 2008-April 4 2008
Firstpage :
1821
Lastpage :
1824
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. Existing methods 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.
Keywords :
acoustic signal processing; array signal processing; blind source separation; independent component analysis; maximum likelihood estimation; statistical distributions; unsupervised learning; a priori knowledge; acoustic signal processing; acoustic source separation; array signal processing; convolutive mixtures; frequency domain; independent component analysis; maximum a posteriori ICA; maximum likelihood derivation; permutation ambiguity; permutation problem; speech signals; unified statistical framework; unmixing matrix; unsupervised learning; Acoustic signal processing; Array signal processing; Computer science; Frequency domain analysis; Frequency estimation; Independent component analysis; Maximum likelihood estimation; Source separation; Speech; Vectors; Acoustic signal processing; Array signal processing; Unsupervised learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on
Conference_Location :
Las Vegas, NV
ISSN :
1520-6149
Print_ISBN :
978-1-4244-1483-3
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2008.4517986
Filename :
4517986
Link To Document :
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