Soft thresholding for DCT speech enhancement

2002 ◽  
Vol 38 (24) ◽  
pp. 1605 ◽  
Author(s):  
S. Salahuddin ◽  
S.Z. Al Islam ◽  
Md.K. Hasan ◽  
M.R. Khan
Keyword(s):  
Speech Enhancement ◽  
Soft Thresholding

EMD based soft-thresholding for speech enhancement

2007 ◽  
Author(s):  
Erhan Deger ◽  
Md. Khademul Islam Molla ◽  
Keikichi Hirose ◽  
Nobuaki Minematsu ◽  
Md. Kamrul Hasan
Keyword(s):  
Speech Enhancement ◽  
Soft Thresholding

scholarly journals Subband DCT and EMD Based Hybrid Soft Thresholding for Speech Enhancement

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Erhan Deger ◽  
Md. Khademul Islam Molla ◽  
Keikichi Hirose ◽  
Nobuaki Minematsu ◽  
Md. Kamrul Hasan
Keyword(s):  
Speech Enhancement ◽  
Time Domain ◽  
Noise Variance ◽  
Intrinsic Mode Functions ◽  
Soft Thresholding ◽  
Second Stage ◽  
Mode Decomposition ◽  
Dct Domain ◽  
Snr Improvement ◽  
Thresholding Algorithm

This paper presents a two-stage soft thresholding algorithm based on discrete cosine transform (DCT) and empirical mode decomposition (EMD). In the first stage, noisy speech is decomposed into eight frequency bands and a specific noise variance is calculated for each one. Based on this variance, each band is denoised using soft thresholding in DCT domain. The remaining noise is eliminated in the second stage through a time domain soft thresholding strategy adapted to the intrinsic mode functions (IMFs) derived by applying EMD on the signal obtained from the first stage processing. Significantly better SNR improvement and perceptual speech quality results for different noise types prove the superiority of the proposed algorithm over recently reported techniques.

DCT speech enhancement with hard and soft thresholding criteria

2002 ◽  
Vol 38 (13) ◽  
pp. 669 ◽  
Author(s):  
M.K. Hasan ◽  
M.S.A. Zilany ◽  
M.R. Khan
Keyword(s):  
Speech Enhancement ◽  
Soft Thresholding

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

2021 ◽  
Author(s):  
Mourad Talbi ◽  
Riadh Baazaoui ◽  
Med Salim Bouhlel
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Speech Enhancement ◽  
Speech Signal ◽  
Signal To Noise Ratio ◽  
Spectral Amplitude ◽  
Enhancement Technique ◽  
Soft Thresholding ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

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).

Implementation Of Advanced Speech Enhancement System On Tms320c6713 Dsk System

2011 ◽  
Vol 1 (12) ◽  
pp. 74-76
Author(s):  
N B Umashankar N B Umashankar ◽  
◽  
Anand Jatti Anand Jatti ◽  
Dr. S.C. Prasanakumar Dr. S.C. Prasanakumar
Keyword(s):  
Speech Enhancement
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