Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Kernel-based nonlinear blind source separation
Neural Computation
Three easy ways for separating nonlinear mixtures?
Signal Processing - Special issue on independent components analysis and beyond
MISEP - Linear and nonlinear ICA based on mutual information
The Journal of Machine Learning Research
Separating a Real-Life Nonlinear Image Mixture
The Journal of Machine Learning Research
Blind identification of a linear-quadratic model using higher-order statistics
ICASSP '93 Proceedings of the Acoustics, Speech, and Signal Processing, 1993. ICASSP-93 Vol 4., 1993 IEEE International Conference on - Volume 04
Signal Processing - Special issue: Information theoretic signal processing
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 05
Blind separation of nonlinear mixtures by variational Bayesian learning
Digital Signal Processing
International Journal of Remote Sensing
Blind separation of linear-quadratic mixtures of real sources using a recurrent structure
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 of a class of nonlinear mixtures
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Handbook of Blind Source Separation: Independent Component Analysis and Applications
Handbook of Blind Source Separation: Independent Component Analysis and Applications
Source separation in post-nonlinear mixtures
IEEE Transactions on Signal Processing
A generic framework for blind source separation in structurednonlinear models
IEEE Transactions on Signal Processing
Inversion of Polynomial Systems and Separation of Nonlinear Mixtures of Finite-Alphabet Sources
IEEE Transactions on Signal Processing - Part II
Blind separation of mixture of independent sources through aquasi-maximum likelihood approach
IEEE Transactions on Signal Processing
Nonlinear blind source separation using a radial basis function network
IEEE Transactions on Neural Networks
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While most reported blind source separation methods concern linear mixtures, we here address the nonlinear case. In the first part of this paper, we introduced a general class of nonlinear mixtures which can be inverted using recurrent networks. That part was focused on separating structures themselves and therefore on the non-blind configuration, whereas the current paper addresses the estimation of the parameters of this large class of structures in a blind context. We propose a maximum likelihood approach to this end. The main advantage of this approach is that it exploits the knowledge of the parametric model of mixing transformation in the separation procedure while its implementation does not require the knowledge of the explicit inverse of the model because the separating structure can be designed using a recurrent network. In particular, we illustrate in detail the proposed approach for a linear-quadratic mixture by using an extended recurrent network with self-feedback parameters which guarantee its local stability. Simulation results show the very good performance of the proposed algorithm.