Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Natural gradient works efficiently in learning
Neural Computation
Flexible Independent Component Analysis
Journal of VLSI Signal Processing Systems
Blind Deconvolution, Information Maximization and Recursive Filters
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
New Geometrical Approach for Blind Separation of Sources Mapped to a Neural Network
NICROSP '96 Proceedings of the 1996 International Workshop on Neural Networks for Identification, Control, Robotics, and Signal/Image Processing (NICROSP '96)
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
Source separation in post-nonlinear mixtures
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
A generic framework for blind source separation in structurednonlinear models
IEEE Transactions on Signal Processing
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
Information geometry on hierarchy of probability distributions
IEEE Transactions on Information Theory
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Multilayer feedforward networks with adaptive spline activation function
IEEE Transactions on Neural Networks
Nonlinear blind source separation using a radial basis function network
IEEE Transactions on Neural Networks
Blind signal processing by complex domain adaptive spline neural networks
IEEE Transactions on Neural Networks
A recurrent ICA approach to a novel BSS convolutive nonlinear problem
WIRN'05 Proceedings of the 16th Italian conference on Neural Nets
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This paper introduces a novel independent component analysis (ICA) approach to the separation of nonlinear convolutive mixtures. The proposed model is an extension of the well-known post nonlinear (PNL) mixing model and consists of the convolutive mixing of PNL mixtures. Theoretical proof of existence and uniqueness of the solution under proper assumptions is provided. Feedforward and recurrent demixing architectures based on spline neurons are introduced and compared. Source separation is performed by minimizing the mutual information of the output signals with respect to the network parameters. More specifically, the proposed architectures perform on-line nonlinear compensation and score function estimation by proper use of flexible spline nonlinearities, yielding a significant performance improvement in terms of source pdf matching and algorithm speed of convergence. Experimental tests on different signals are described to demonstrate the effectiveness of the proposed approach.