NDLI: Weight Adjusted Tensor Method for Blind Separation of Underdetermined Mixtures of Nonstationary Sources

Content Provider	IEEE Xplore Digital Library
Author	Tichavsky, P. Koldovsky, Z.
Copyright Year	1991
Abstract	In this paper, a novel algorithm to blindly separate an instantaneous linear underdetermined mixture of nonstationary sources is proposed. It means that the number of sources exceeds the number of channels of the available data. 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 with respect to the remaining signals. 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 nine linear instantaneous mixtures of these signals.
Sponsorship	IEEE Signal Processing Society
Starting Page	1037
Ending Page	1047
Page Count	11
File Size	973691
File Format	PDF
ISSN	1053587X
Volume Number	59
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-03-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Tensile stress Covariance matrix Speech Matrix decomposition Brain modeling Signal to noise ratio Estimation underdetermined mixture Blind source separation Cramér–Rao lower bound multilinear models nonstationary processes tensor decomposition
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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