New modified Bat algorithm for blind speech enhancement in time domain

Abstract

We address the speech enhancement problem for dual convolutif mixed channel by viewing it in a blind separation sources setting. One widely used technique to separate mixed signals is to apply adaptive filtering, the challenge is to identify an unknown finite impulse response. traditionally we apply a gradient-based algorithm to adapt filter coefficients. However, such algorithm often suffers from premature convergence ,when using large filters and non-stationary inputs ,  leading to the so-called local minimum problem , which affects the quality of  enhanced signals significatively .one alternative to overcome this problem is to apply a population-based metaheuristic algorithms in which filter coefficients are adapted iteratively  by minimizing a cost function .But even with this metaheuristic based solution, local minimum problem at large filters still exists. In order to avoid local minima and improve the chance to reach the global solution, we propose in this paper, a novel algorithm called a modified bat algorithm to render the search process efficiently by enhancing its capability of exploration and exploitation. Several experiments under different noise types are carried out using our proposed modified bat algorithm in comparison with some of the popular state-of-the-art algorithms. The enhanced signals obtained by each algorithm at the separation process outputs, show good behavior and superiority of our proposed algorithm. In terms of system misalignment, as well as a segmental signal-to-noise ratio.