A novel speech enhancement approach based on Singular Value Decomposition and Genetic Algorithm

Abstract

The Singular Value Decomposition (SVD) is a powerful tool used for subspace division. In this paper a novel approach for speech signal enhancement is presented which is based on SVD and Genetic Algorithm (GA). The method is derived from the effects of environmental noises on the singular vectors as well as the singular values of a clean speech. This article reviews the existing approaches for subspace estimation and proposes novel techniques for effectively enhancing the singular values and vectors of a noisy speech. The proposed approach clearly results in a considerable attenuation of the noise as well as retrieving the quality of the original speech. The efficiency of our proposed method is affected by a number of crucial parameters which are optimally set by utilizing the GA. Extensive sets of experiments have been carried out for both of additive white Gaussian noise as well as different types of realistic colored noise cases. The results of applying six superior speech enhancement methods are then evaluated by the objective (SNR) and subjective (PESQ) measures.