A new efficient backward BSS crosstalk-resistant algorithm for automatic blind speech quality enhancement

In last 10 years, several noise reduction (NR) algorithms have been proposed to be combined with the blind source separation techniques to separate speech and noise signals from blind noisy observations. More often, techniques use voice activity detector (VAD) systems for the optimal solution. In this paper, we propose a new backward blind source separation (BBSS) structure that uses the input correlation properties to provide: (i) high convergence rates and good tracking capabilities, since the acoustic environments imply long and time-variant noise paths, and (ii) low misalignment and robustness against different noise type variations and double-talk. The proposed algorithm has an automatic behavior to enhance noisy speech signals, and do not need any VAD systems to separate speech and noise signals. The obtained results in terms of several objective criteria show the good performance properties of the proposed algorithm in comparison with state-of-the-art algorithms.