Blind Speech Separation in Time-Domain Using Block-Toeplitz Structure of Reconstructed Signal Matrices

Methods for Blind Source Separation (BSS) aim at recovering signals from their mixture without prior knowledge about the signals and the mixing system. Among others, they provide tools for enhancing speech signals when they are disturbed by unknown noise or other interfering signals in the mixture. This paper considers a recent time-domain BSS method that is based on a complete decomposition of a signal subspace into components that should be independent. The components are used to reconstruct images of original signals using an ad hoc weighting, which influences the final performance of the method markedly. We propose a novel weighting scheme that utilizes block-Toeplitz structure of signal matrices and relies thus on an established property. We provide experiments with blind speech separation and speech recognition that prove the better performance of the modified BSS method.