A robust H? H ? learning approach to blind separation of signals
A robust estimation technique based on the H? H ? 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? H ? filter to the BSS model with state-space representation. The motivation behind applying H? H ? 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? H ? filter, Kalman filter, ICA based on mutual information and Nonlinear PCA establishes the robustness of the proposed H? H ? approach.
blind source separation (bss) non-stationary mixing kalman filter h? h ? filter h? h ? h? h ? h ?