Naslov
Blind Signal Deconvolution as an Instantaneous Blind Separation of Statistically Dependent Sources

Autori
Kopriva, Ivica

Izvornik
Lecture notes in computer science (0302-9743) 4666 (2007); 504-511

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Blind deconvolution ; Blind source separation ; Independent component analysis ; Instantaneous mixture model ; Statistically dependent sources

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Matematika, Računarstvo, Informacijske i komunikacijske znanosti

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