Blind Signal Separation Using Steepest Descent Method

Abstract

Abstract-A method that significantly improves the convergence rate of the gradient-based blind signal separation (BSS) algorithm for convolutive mixtures is proposed. The proposed approach is based on the steepest descent algorithm suitable for constrained BSS problems, where the constraints are included to ease the permutation effects associated with the convolutive mixtures. In addition, the method is realized using a modified golden search method plus parabolic interpolation, and this allows the optimum step size to be determined with only a few calculations of the cost function. Evaluation of the proposed procedure in simulated environments and in a real room environment shows that the proposed method results in significantly faster convergence for the BSS when compared with a fixed step-size gradient-based algorithm. In addition, for blind signal extraction where only a main speech source is desired, a combined scheme consisting of the proposed BSS and a postprocessor, such as an adaptive noise canceller, offers impressive noise suppression levels while maintaining low-target signal distortion levels.

Sensor placement optimization of blind source separation in a wireless acoustic sensor network via hybrid descent methods

“…Owing to second-order processing only, this method is computationally efficient. A fast convergent BSS algorithm based on the SOS method was presented in [18]. An alternative beamspace SOS method was introduced by [19] where a priori spatial information was embedded in the formulation as a preprocessor to help improve the separation performance.…”