Introduction

Benesty, Jacob; Cohen, Israel

doi:10.1007/978-3-319-67020-1_1

Cited by 1 publication

(1 citation statement)

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“…This approach employs at least two microphones configuration in order to update the adaptive filtering algorithms. Also, a multi-microphone speech enhancement technique was proposed for the same aim and ameliorated the existing one-channel and two-channel speech enhancement and noise reduction adaptive algorithms [33,34]. Numerous research works highlighted the problem of speech enhancement on a simple problem of mixing and unmixing signals with convolutive and instantaneous noisy observations [35][36][37].…”

Section: Introductionmentioning

confidence: 99%

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

Talbi¹,

Baazaoui²,

Bouhlel³

2021

Deep Learning Applications

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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).

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Section: Introductionmentioning

confidence: 99%