Improved single-channel noise reduction method of speech by blind source separation

Abstract

An improved method of single-channel noise reduction by blind source separation (BSS) is discussed in this paper. A method of and a system for noise suppression, consisting of two adaptive filters for the first noise-reduced speech and the estimated noise-dominant signal, are developed. Initially, we reduce the noise level by the weighted noise subtraction (WNS) method and obtain the first noise-reduced speech. We consider the square of the complement of the estimated noise degree as a weighting factor during the subtraction. The least-mean-squares (LMS) algorithm that is based on the steepest descent method is implemented in adaptive filtering. The method addresses the situations in which the input signal-to-noise ratio (SNR) varies substantially and performing the specified number of iterations of the LMS algorithm for each SNR is time-consuming. Therefore, we propose a function that can be used to estimate the number of iterations required for a given value of the noise degree. The proposed iteration number reduces the computational time and minimizes the signal regeneration problem. Moreover, good efficiency of the algorithm is achieved by appropriate block length processing. The experimental results confirm the improved performance of the proposed WNS+BSS method.