Optimized Singular Vector Denoising Approach for Speech Enhancement

In this paper, a novel approach for speech signal enhancement is presented. This approach employs singular value decomposition (SVD) to overlook noise subspace and uses Genetic Algorithm (GA) tooptimally set the essential parameters. The method is elicited by analyzing the effects of environmental noiseson the singular vectors as well as the singular values of clean speech signals. This article reviews the existingapproaches for subspace estimation and proposes novel techniques for effectively enhancing the singularvalues and vectors of a noisy speech. This results in a considerable attenuation of the noise and retainingquality of the original speech. The efficiency of our proposed method is affected by a number of parameterswhich are optimally set by utilizing the GA. Extensive sets of experiments have been carried out on speech signals impaired by additive white Gaussian noise and/or different types of realistic coloured noises. The results of applying the six superior speech enhancement methods are compared using the objective (SNR) and subjective (PESQ) measures