Speech Enhancement Based on Bionic Wavelet Transform and Correlation Denoising

2014 ◽  
Vol 912-914 ◽  
pp. 1386-1390
Author(s):  
Li Ming Wu ◽  
Yao Fei Li ◽  
Fu Jian Li ◽  
Xin Luo
Keyword(s):  
Correlation Function ◽  
Wavelet Transform ◽  
Speech Enhancement ◽  
Enhancement Effect ◽  
Wavelet Coefficients ◽  
Enhancement Method ◽  
Simulation Results

A new speech enhancement method based on bionic wavelet transform is presented here. Voice signals with noise would be bionic wavelet coefficients by bionic wavelet transform, then, the purpose of speech enhancement can be achieved by means of the bionic wavelet coefficients based on the improved correlation function processing. The simulation results show that the method under the condition of various noises is good speech enhancement effect. Keywords: Speech enhancement; BWT; Correlation de-noising

A Speech Enhancement Method in Low SNR Environment

2013 ◽  
Vol 645 ◽  
pp. 179-183
Author(s):  
Yao Qi Wang ◽  
Xiao Peng Wang ◽  
Raji Rafiu King
Keyword(s):  
Wavelet Transform ◽  
Speech Enhancement ◽  
Experimental Results ◽  
Threshold Function ◽  
Morphological Filter ◽  
Wavelet Domain ◽  
Low Snr ◽  
Morphological Filtering ◽  
Enhancement Method ◽  
Enhancement Algorithm

A new method of speech enhancement is proposed based on morphological filter and wavelet transform. The system begins by first conducting morphological filtering, then distinguishing the unvoiced, voiced and noise using TEO in the wavelet domain. It then executes wavelet transform using different threshold on multiscale, and at the same time to improve the threshold function. Experimental results showed that the method not only suppressed noise effectively but also reduced the loss of the unvoiced. It also not only enhanced SNR, but also improved voice clarity and comfort. The merits it espouses makes it an effective speech enhancement algorithm.

scholarly journals A NEW SPEECH ENHANCEMENT TECHNIQUE USING PERCEPTUAL CONSTRAINED SPECTRAL WEIGHTING FACTORS

2012 ◽  
pp. 212-217
Author(s):  
T. Muni Kumar ◽  
M.B.Rama Murthy ◽  
Ch.V.Rama Rao ◽  
K.Srinivasa Rao
Keyword(s):  
Speech Enhancement ◽  
Weighting Factor ◽  
Wiener Filtering ◽  
Noise Masking ◽  
Enhancement Technique ◽  
Enhancement Method ◽  
Simulation Results ◽  
Spectral Weighting ◽  
Better Than

This paper deals with musical noise result from perceptual speech enhancement type algorithms and especially wiener filtering. Although perceptual speech enhancement methods perform better than the non perceptual methods, most of them still return annoying residual musical noise. This is due to the fact that if only noise above the noise masking threshold is filtered then noise below the noise masking threshold can become audible if its maskers are filtered. It can affect the performance of perceptual speech enhancement method that process audible noise only. In order to overcome this drawback here proposed a new speech enhancement technique. It aims to improve the quality of the enhanced speech signal provided by perceptual wiener filtering by controlling the latter via a second filter regarded as a psychoacoustically motivated weighting factor. The simulation results shows that the performance is improved compared to other perceptual speech enhancement methods

Medical Image Enhancement Method Based on Wavelet Transform and Mode Decomposition

2013 ◽  
Vol 389 ◽  
pp. 930-935 ◽  
Author(s):  
Ao Shuang Dong ◽  
Bin Bin Lou ◽  
Hui Yan Jiang ◽  
Qiang Tong ◽  
Guang Ming Yang ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Image Enhancement ◽  
Empirical Mode Decomposition ◽  
Medical Image ◽  
Enhancement Effect ◽  
Mode Decomposition ◽  
Enhancement Method ◽  
Image Edge ◽  
Bidimensional Empirical Mode Decomposition ◽  
Medical Image Enhancement

Traditional medical image enhancement method has some disadvantages. They can not significantly improve the medical image edge, texture and detailed information. Besides the enhancement effect is susceptible to interference noise information. This paper proposed enhancement algorithms combining bidimensional empirical mode decomposition and the wavelet edge enhancement method. The first step is using the method of bidimensional empirical mode decomposition to process medical image, achieve image information with different frequency. And then our method using wavelet transform to enhance different frequency image edge, texture information. Through the comparison of proposed method with the existing method, it has been verified the proposed method has a better effect in the detail enhancement of medical images.

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

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Mourad Talbi ◽  
Med Salim Bouhlel
Keyword(s):  
Wavelet Transform ◽  
Speech Enhancement ◽  
Hearing Aids ◽  
Speech Signal ◽  
Minimum Mean Square Error ◽  
Voice Conversion ◽  
Spectral Amplitude ◽  
Wavelet Coefficients ◽  
Enhancement Technique ◽  
Perceptual Evaluation

Speech enhancement has gained considerable attention in the employment of speech transmission via the communication channel, speaker identification, speech-based biometric systems, video conference, hearing aids, mobile phones, voice conversion, microphones, and so on. The background noise processing is needed for designing a successful speech enhancement system. In this work, a new speech enhancement technique based on Stationary Bionic Wavelet Transform (SBWT) and Minimum Mean Square Error (MMSE) Estimate of Spectral Amplitude is proposed. This technique consists at the first step in applying the SBWT to the noisy speech signal, in order to obtain eight noisy wavelet coefficients. The denoising of each of those coefficients is performed through the application of the denoising method based on MMSE Estimate of Spectral Amplitude. The SBWT inverse, S B W T − 1 , is applied to the obtained denoised stationary wavelet coefficients for finally obtaining the enhanced speech signal. The proposed technique’s performance is proved by the calculation of the Signal to Noise Ratio (SNR), the Segmental SNR (SSNR), and the Perceptual Evaluation of Speech Quality (PESQ).

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