By Topic

Bayesian blind MIMO deconvolution of nonstationary autoregressive sources mixed through all-pole channels

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Hopgood, J.R. ; Dept. of Eng., Cambridge Univ., UK

Blind deconvolution is fundamental in signal processing applications and still remains a challenging problem. In particular, blind dereverberation is necessary for applications set in acoustic environments. In this setting, a temporally-correlated observed signal whose signal-value has infinite support is modelled as the convolutive mixture of unknown source signals with an unknown channel. Multi-channel blind deconvolution is tackled by extending a method that has previously been successfully applied to the single-channel scenario. To avoid any channel-source identification ambiguities, each nonstationary source is modelled by block stationary AR process, and each channel path by a stationary subband all-pole filter. Robust and accurate estimates of the channel are obtained using Bayesian techniques, and an estimate of the original signal is obtained by inverse filtering the observed convolved signal. Simulation results are included, and it is expected that further results is presented at

Published in:

Statistical Signal Processing, 2003 IEEE Workshop on

Date of Conference:

28 Sept.-1 Oct. 2003