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Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Independent component analysis by general nonlinear Hebbian-like learning rules
Signal Processing - Special issue on neural networks
High-order contrasts for independent component analysis
Neural Computation
Neural Computation
Blind separation of filtered sources using state-space approach
Proceedings of the 1998 conference on Advances in neural information processing systems II
Statistical Digital Signal Processing and Modeling
Statistical Digital Signal Processing and Modeling
Blind Source Separation Using Least-Squares Type Adaptive Algorithms
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
A class of neural networks for independent component analysis
IEEE Transactions on Neural Networks
Neural Processing Letters
Temporally correlated source separation using variational Bayesian learning approach
Digital Signal Processing
Bayesian nonstationary source separation
Neurocomputing
A robust H∞ learning approach to blind separation of signals
Digital Signal Processing
Probabilistic geometric approach to blind separation of time-varying mixtures
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
State inference in variational bayesian nonlinear state-space models
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
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ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Variational bayesian method for temporally correlated source separation
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Wavelet and Unsupervised Learning Techniques for Experimental Biomedical Data Processing
International Journal of Measurement Technologies and Instrumentation Engineering
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This article addresses the problem of blind source separation from time-varying noisy mixtures using a state variable model and recursive estimation. An estimate of each source signal is produced real time at the arrival of new observed mixture vector. The goal is to perform the separation and attenuate noise simultaneously, as well as to adapt to changes that occur in the mixing system. The observed data are projected along the eigenvectors in signal subspace. The subspace is tracked real time. Source signals are modeled using low-order AR (autoregressive) models, and noise is attenuated by trading off between the model and the information provided by measurements. The type of zero-memory nonlinearity needed in separation is determined on-line. Predictor-corrector filter structures are proposed, and their performance is investigated in simulation using biomedical and communications signals at different noise levels and a time-varying mixing system. In quantitative comparison to other widely used methods, significant improvement in output signal-to-noise ratio is achieved.