Fuzzy logic based variable step size algorithm for blind delayed source separation

Convergence of blind delayed source separation algorithms, which use constant learning rates, is known to be slow. We propose a fuzzy logic based approach to adaptively select the learning rates, for estimates of delays and cross-weights, in the blind delayed source separation algorithm. We make use of the state of independence of the separated outputs. We also propose a performance index to measure the convergence of the blind delayed source separation algorithm. Simulation results show the improved performance of the proposed algorithm over the conventional delayed source separation algorithm under stationary as well as non-stationary mixing conditions.

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