NDLI: Flexible multichannel blind deconvolution, an investigation

Content Provider	IEEE Xplore Digital Library
Author	Ah Chung Tsoi Liang Suo Ma
Copyright Year	2003
Description	Author affiliation: Office of Pro-Vice-Chancellor, Wollongong Univ., NSW, Australia (Ah Chung Tsoi; Liang Suo Ma)
Abstract	In this paper, we consider the issue of devising a flexible nonlinear function for multichannel blind deconvolution. In particular, we consider the underlying assumption of the source probability density functions. We consider two cases, when the source probability density functions are assumed to be uni-modal, and multimodal respectively. In the unimodal case, there are two approaches: Pearson function and generalized exponential function. In the multimodal case, there are three approaches: mixture of Gaussian functions, mixture of Pearson functions, and mixture of generalized exponential functions. It is demonstrated through an illustrating example that the assumption on the source probability density functions gives rise to different performances of source separation algorithms for the multichannel blind deconvolution problem. Further it is observed that these performance differences are not large, indicating that the current formulation of multichannel blind deconvolution problems is robust with respect to the underlying assumption of source probability density functions. It is further speculated that one of the discriminating features among various source separation algorithms appears to be the relative computational efficiencies of various approximation schemes. In other words, the discriminating feature of various source separation algorithms based on assumptions on the source probability density function appears to be an implementation issue rather than one of a theoretical concern.
Starting Page	349
Ending Page	358
File Size	416847
Page Count	10
File Format	PDF
ISBN	0780381777
ISSN	10893555
DOI	10.1109/NNSP.2003.1318034
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-09-17
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Deconvolution Probability density function Signal processing algorithms Source separation Parameter estimation Australia Robustness Blind source separation Neural networks Spline
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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