Weight adjusted tensor method for blind separation of underdetermined mixtures of nonstationary sources

0356666 - ÚTIA 2011 RIV US eng J - Journal Article
Tichavský, Petr - Koldovský, Zbyněk
Weight adjusted tensor method for blind separation of underdetermined mixtures of nonstationary sources.
IEEE Transactions on Signal Processing. Roč. 59, č. 3 (2011), s. 1037-1047. ISSN 1053-587X. E-ISSN 1941-0476
R&D Projects: GA MŠMT 1M0572; GA ČR GA102/09/1278
Institutional research plan: CEZ:AV0Z10750506
Keywords : blind source separation * tensor decomposition * Cramer-Rao lower bound
Subject RIV: BB - Applied Statistics, Operational Research
Impact factor: 2.628, year: 2011
http://library.utia.cas.cz/separaty/2011/SI/tichavsky-0356666.pdf

In this paper, a novel algorithm to blindly separate an instantaneous linear underdetermined mixture of nonstationary sources is proposed. The separation is based on the working assumption that the sources are piecewise stationary with a different variance in each block. It proceeds in two steps: (1) estimating the mixing matrix, and (2) computing the optimum beamformer in each block to maximize the signal-to-interference ratio of each separated signal. Estimating the mixing matrix is accomplished through a specialized tensor decomposition of the set of sample covariance matrices of the received mixture in each block. It utilizes optimum weighting, which allows statistically efficient (CRB attaining) estimation provided that the data obey the assumed Gaussian piecewise stationary model. In simulations, performance of the algorithm is successfully tested on blind separation of 16 speech signals from 9 linear instantaneous mixtures of these signals.
Permanent Link: http://hdl.handle.net/11104/0195127