Speech Enhancement Based on LWT and Artificial Neural Network and Using MMSE Estimate of Spectral Amplitude

Abstract

In this chapter, we will detail a new speech enhancement technique based on Lifting Wavelet Transform (LWT) and Artifitial Neural Network (ANN). This technique also uses the MMSE Estimate of Spectral Amplitude. It consists at the first step in applying the LWTto the noisy speech signal in order to obtain two noisy details coefficients, cD1 and cD2 and one approximation coefficient, cA2. After that, cD1 and cD2 are denoised by soft thresholding and for their thresholding, we need to use suitable thresholds, thrj,1≤j≤2. Those thresholds, thrj,1≤j≤2, are determined by using an Artificial Neural Network (ANN). The soft thresholding of those coefficients, cD1 and cD2, is performed in order to obtain two denoised coefficients, cDd1 and cDd2 . Then the denoising technique based on MMSE Estimate of Spectral Amplitude is applied to the noisy approximation cA2 in order to obtain a denoised coefficient, cAd2. Finally, the enhanced speech signal is obtained from the application of the inverse of LWT, LWT−1 to cDd1, cDd2 and cAd2. The performance of the proposed speech enhancement technique is justified by the computations of the Signal to Noise Ratio (SNR), Segmental SNR (SSNR) and Perceptual Evaluation of Speech Quality (PESQ).

A New Speech Enhancement Technique Based on Stationary Bionic Wavelet Transform and MMSE Estimate of Spectral Amplitude

“…In the following, we will compare the proposed technique with our previous speech enhancement approach which is based on LWT and ANN and uses MMSE [26]. e first difference between the speech enhancement technique proposed in this work and our previous approach is that they use two completely different wavelet transforms which are the SBWT for the technique proposed in this paper and the LWT for our previous approach proposed in [26].…”

“…In the following, we will compare the proposed technique with our previous speech enhancement approach which is based on LWT and ANN and uses MMSE [26]. e first difference between the speech enhancement technique proposed in this work and our previous approach is that they use two completely different wavelet transforms which are the SBWT for the technique proposed in this paper and the LWT for our previous approach proposed in [26]. e second difference between these two techniques is that the denoising approach based on MMSE Estimate of Spectral Amplitude is applied [22] to all stationary bionic wavelet coefficients for the technique proposed in this paper.…”

“…e second difference between these two techniques is that the denoising approach based on MMSE Estimate of Spectral Amplitude is applied [22] to all stationary bionic wavelet coefficients for the technique proposed in this paper. However, we apply this approach [22] only to the approximation coefficient for our previous speech enhancement technique proposed in [26]. e latter also uses an The Denoised Stationary Bionic Coefficients present the results obtained from the computation of SNR, SSNR, and PESQ for the two techniques.…”

“…(iii) e denoising technique based on MMSE Estimate of Spectral Amplitude [22]. (iv) Our previous speech enhancement technique based on LWT and Artificial Neural Network (ANN) and using MMSE Estimate of Spectral Amplitude [26].…”

“…e fourth technique which is based on LWT and ANN [27][28][29] and uses MMSE Estimate of Spectral Amplitude [26] can be summarized by the following steps:…”