Počet záznamů: 1

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

  1. 1.
    0356666 - UTIA-B 2011 RIV US eng J - Článek v odborném periodiku
    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
    Grant CEP: GA MŠk 1M0572; GA ČR GA102/09/1278
    Výzkumný záměr: CEZ:AV0Z10750506
    Klíčová slova: blind source separation * tensor decomposition * Cramer-Rao lower bound
    Kód oboru RIV: BB - Aplikovaná statistika, operační výzkum
    Impakt faktor: 2.628, rok: 2011
    http://library.utia.cas.cz/separaty/2011/SI/tichavsky-0356666.pdf 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.
    Trvalý link: http://hdl.handle.net/11104/0195127