Space or time adaptive signal processing by neural network models
AIP Conference Proceedings 151 on Neural Networks for Computing
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
A fast fixed-point algorithm for independent component analysis
Neural Computation
Natural gradient works efficiently in learning
Neural Computation
Advances in Independent Component Analysis
Advances in Independent Component Analysis
Blind Separation of Multiple Speakers in a Multipath Environment
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 1 - Volume 1
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
Blind source separation-semiparametric statistical approach
IEEE Transactions on Signal Processing
Blind Source Recovery in a State-Space Famework: Algorithms for Static and Dynamic Environments
Neural Processing Letters
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Blind Source Recovery (BSR) denotes recovery of original sources/signals from environments that may include convolution, temporal variation, and even nonlinearity. It also infers the recovery of sources even in the absence of precise environment identifiability. This paper describes, in a comprehensive fashion, a generalized BSR formulation achieved by the application of stochastic optimization principles to the Kullback-Liebler divergence as a performance functional subject to the constraints of the general (i.e., nonlinear and time-varying) state space representation. This technique is used to derive update laws for nonlinear time-varying dynamical systems, which are subsequently specialized to time-invariant and linear systems. Further, the state space demixing network structures have been exploited to develop learning rules, capable of handling most filtering paradigms, which can be conveniently extended to nonlinear models. In the special cases, distinct linear state-space algorithms are presented for the minimum phase and non-minimum phase mixing environment models. Conventional (FIR/IIR) filtering models are subsequently derived from this general structure and are compared with material in the recent literature. Illustrative simulation examples are presented to demonstrate the online adaptation capabilities of the developed algorithms. Some of this reported work has also been implemented in dedicated hardware/software platforms.