This paper presents a study on the blind separation of a convoluted mixture of speech signals using Frequency Domain Independent Component Analysis (FDICA) algorithm based on the negentropy maximization of Time Frequency Series of Speech (TFSS). The comparative studies on the negentropy approximation of TFSS using generalized Higher Order Statistics (HOS) of different nonquadratic, nonlinear functions are presented. A new nonlinear function based on the statistical modeling of TFSS by exponential power functions has also been proposed. The estimation of standard error and bias, obtained using the sequential delete-one jackknifing method, in the approximation of negentropy of TFSS by different nonlinear functions along with their signal separation performance indicate the superlative power of the exponential-power-based nonlinear function. The proposed nonlinear function has been found to speed-up convergence with slight improvement in the separation quality under reverberant conditions.

This paper deals with the statistical modeling of a Time-Frequency Series of Speech (TFSS), obtained by Short-Time Fourier Transform (STFT) analysis of the speech signal picked up by a linear microphone array with two elements. We have attempted to find closer match between the distribution of the TFSS and theoretical distributions like Laplacian Distribution (LD), Gaussian Distribution (GD) and Generalized Gaussian Distribution (GGD) with parameters estimated from the TFSS data. It has been found that GGD provides the best models for real part, imaginary part and polar magnitudes of the time-series of the spectral components. The distribution of the polar magnitude is closer to LD than that of the real and imaginary parts. The distributions of the real and imaginary parts of TFSS correspond to strongly LD. The phase of the TFSS has been found uniformly distributed. The use of GGD based model as PDF in the fixed-point Frequency Domain Independent Component Analysis (FDICA) provides better separation performance and improves convergence speed significantly.