Paper Info
Title
Blind signal deconvolution as an instantaneous blind separation of statistically dependent sources
Abstract
We propose a novel approach to blind signal deconvolution. It is based on the approximation of the source signal by Taylor series expansion and use of a filter bank-like transform to obtain multichannel representation of the observed signal. Currently, as an ad hoc choice a wavelet packets filter bank has been used for that purpose. This leads to multi-channel instantaneous linear mixture model (LMM) of the observed signal and its temporal derivatives converting single channel blind deconvolution (BD) problem into instantaneous blind source separation (BSS) problem with statistically dependent sources. The source signal is recovered provided it is a non-Gaussian, non-stationary and non-independent identically distributed (i.i.d.) process. The important property of the proposed approach is that order of the channel filter does not have to be known or estimated. We demonstrate viability of the proposed concept by blind deconvolution of the speech and music signals passed through a linear low-pass channel.
Year
DOI
Venue
2007
10.1007/978-3-540-74494-8_63
ICA
Keywords
Field
DocType
instantaneous blind separation,blind signal deconvolution,linear low-pass channel,single channel,source signal,instantaneous linear mixture model,dependent source,instantaneous blind source separation,blind deconvolution,channel filter,observed signal,mixture model,filter bank,taylor series expansion,low pass,independent component analysis,blind source separation
Blind deconvolution,Filter bank,Algorithm,Dependent source,Deconvolution,Speech recognition,Independent component analysis,Wavelet packet decomposition,Blind equalization,Blind signal separation,Mathematics
Conference
Volume
ISSN
ISBN
4666
0302-9743
3-540-74493-2
Citations 
PageRank 
References 
1
0.36
6
Authors
1
Name
Order
Citations
PageRank
Ivica Kopriva114616.60