Blind separation of linear-quadratic mixtures of real sources using a recurrent structure

Authors:
Shahram Hosseini;Yannick Deville
Affiliations:
Laboratoire d'Acoustique, Métrologie, Instrumentation, Université Paul Sabatier, Toulouse Cedex, France 31062;Laboratoire d'Acoustique, Métrologie, Instrumentation, Université Paul Sabatier, Toulouse Cedex, France 31062
Venue:
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Year:
2009

Citing 10
Cited 5

Blind separation of sources, Part 1: an adaptive algorithm based on neuromimetic architecture

Signal Processing
Blind source separation for convolutive mixtures

Signal Processing
Self-adaptive separation of convolutively mixed signals with a recursive structure. Part I: stability analysis and optimization of asymptotic behavior

Signal Processing
Nonlinear independent component analysis: existence and uniqueness results

Neural Networks
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications

Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Three easy ways for separating nonlinear mixtures?

Signal Processing - Special issue on independent components analysis and beyond
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
Blind identification of a linear-quadratic mixture of independent components based on joint diagonalization procedure

ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 05
Source separation in post-nonlinear mixtures

IEEE Transactions on Signal Processing
Analysis of the convergence properties of self-normalized sourceseparation neural networks

IEEE Transactions on Signal Processing

Recurrent networks for separating extractable-target nonlinear mixtures. Part I: Non-blind configurations

Signal Processing
Blind source separation of overdetermined linear-quadratic mixtures

LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Bayesian Source Separation of Linear and Linear-quadratic Mixtures Using Truncated Priors

Journal of Signal Processing Systems
Separation of sparse signals in overdetermined linear-quadratic mixtures

LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Recurrent networks for separating extractable-target nonlinear mixtures. Part II. Blind configurations

Signal Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose an approach for separating linearquadratic mixtures of independent real sources. The method is based on parametric identification of a recurrent separating structure by means of an adaptive algorithm which uses the higher-order statistics of the outputs of this structure. We study the local stability of the recurrent structure and show experimentally that when it is stable at the separating point, it succeeds in separating the sources.