Blind Source Separation by Sparse Decomposition in a Signal Dictionary
Neural Computation
Sparse ICA via cluster-wise PCA
Neurocomputing
A robust method to count and locate audio sources in a stereophonic linear instantaneous mixture
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Blind separation of speech mixtures via time-frequency masking
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Underdetermined blind source separation based on sparse representation
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
A Bayesian Approach for Blind Separation of Sparse Sources
IEEE Transactions on Audio, Speech, and Language Processing
Sparse component analysis and blind source separation of underdetermined mixtures
IEEE Transactions on Neural Networks
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In this paper, we address theoretical limitations in estimating the mixing matrix in noisy Sparse Component Analysis (SCA) in the two-sensor case. We obtain the Cramer-Rao Bound (CRB) error estimation of the mixing matrix based on the observation vector x=(x"1,x"2)^T. Using the Bernoulli-Gaussian (BG) sparse distribution for sources, and some reasonable approximations, the Fisher Information Matrix (FIM) is approximated by a diagonal matrix. Then, the effect of off-diagonal terms in computing the CRB is investigated. Moreover, we compute an oracle CRB versus the blind uniform CRB and show that this is only 3 dB better than the blind uniform CRB. Finally, the CRB, the approximated CRB, the uniform CRB and the oracle CRB are compared to each other and to some of the main mixing matrix estimation methods in the literature. Simulation results show that the approximated CRB is close to the CRB for high SNR@?s. They also show that the approximated CRB is approximately equal to the oracle CRB.