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In this paper the blind separation of speech signals from their convoluted mixtures using frequency domain fixed-point independent component analysis algorithm, based on negentropy maximization, is presented. We also discuss fundamental problems of fixed-point ICA by negentropy maximization arising in the separation of the speech signal due to disobedience of the Central Limit Theorem (CLT) by the mixed speech data in the frequency domain. The experimental evidences show that CLT failure is happening due to the spectral sparseness of sources. We also present a blind method to mitigate the negative effects of this by combining null beamforming with the ICA. This combination gives a good result under the low reverberation conditions

Effect of Central Limit Theorem non-compliance on blind separation of speech by negentropy maximization