Efficient subband fast adaptive algorithm based-backward blind source separation for speech intelligibility enhancement

Abstract

This paper addresses the problem of speech intelligibility enhancement by subband adaptive filtering algorithms in a blind framework. Recently in Djendi and Sayoud (Int J Speech Technol 22:391–406, 2019), we have proposed a subband adaptive algorithm based on the forward blind source separation structure that is efficient for acoustic noise reduction and speech intelligibility enhancement applications. In this paper, we propose a novel subband domain implementation of the backward blind source separation structure combined with a modified version of the fast normalized least mean square (FNLMS) algorithm. The new proposed subband algorithm is efficient in improving the speech signal intelligibility without introducing any distortion at the output. A fair comparison of the proposed backward subband FNLMS algorithm with other fullband type algorithms is presented. This comparison is based on the evaluation of several objective criteria. The obtained results show the best performance of the proposed subband algorithm in terms of speed convergence.