Second Order Nonstationary Source Separation
Journal of VLSI Signal Processing Systems
Robust Blind Source Separation Utilizing Second and Fourth Order Statistics
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Optimization techniques for independent component analysis with applications to EEG data
Quantitative neuroscience
Blind multiuser detection for long-code CDMA systems with transmission-induced cyclostationarity
EURASIP Journal on Wireless Communications and Networking - Special issue on advanced signal processing algorithms for wireless communications
Multiuser channel estimation from higher-order statistical matrix pencil
EURASIP Journal on Applied Signal Processing
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Blind source separation based on cumulants with time and frequency non-properties
IEEE Transactions on Audio, Speech, and Language Processing
The generalized eigendecomposition approach to the blind source separation problem
Digital Signal Processing
On a sparse component analysis approach to blind source separation
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
One-unit second-order blind identification with reference for short transient signals
Information Sciences: an International Journal
Multidimensional Systems and Signal Processing
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For many signal sources such as speech with distinct, nonwhite power spectral densities, second-order statistics of the received signal mixture can be exploited for signal separation. Without knowledge of the noise correlation matrix, we propose a simple and yet effective signal extraction method for signal source separation under unknown temporally white noise. This new and unbiased signal extractor is derived from the matrix pencil formed between output autocorrelation matrices at different delays. Based on the matrix pencil, an ESPRIT-type algorithm is derived to get an optimal solution in the least square sense. Our method is well suited for systems with colored sensor noises and for nonstationary signals