A robust H sub([infinity]) learning approach to blind separation of signals

A robust estimation technique based on the H sub([infinity]) filter (learning) is proposed in this paper to address the instantaneous Blind source separation (BSS) problem in a non-stationary mixing environment. It is assumed that the variations in the mixing system are small. The learning algorithm...

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Bibliographic Details
Published in:Digital signal processing 2010-03, Vol.20 (2), p.410-416410-416
Main Authors: Das, Niva, Routray, Aurobinda, Dash, Pradipta Kishore
Format: Article
Language:English
Subjects:
Algorithms
Blinds
Learning
Nonlinearity
Representations
Robustness
Separation
Sound filters
Online Access:Get full text
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Summary:A robust estimation technique based on the H sub([infinity]) filter (learning) is proposed in this paper to address the instantaneous Blind source separation (BSS) problem in a non-stationary mixing environment. It is assumed that the variations in the mixing system are small. The learning algorithm is obtained by applying H sub([infinity]) filter to the BSS model with state-space representation. The motivation behind applying H sub([infinity]) filter is its robustness towards errors arising out of model uncertainties, parameter variations and noise. The proposed algorithm is applied to both synthetically generated signals and practical sound signals. A performance comparison between the H sub([infinity]) filter, Kalman filter, ICA based on mutual information and Nonlinear PCA establishes the robustness of the proposed H sub([infinity]) approach.
ISSN:1051-2004
DOI:10.1016/j.dsp.2009.07.003