Locally Adaptive Wavelet Thresholding for Speech Enhancement

2013 ◽  
Vol 380-384 ◽  
pp. 3618-3622
Author(s):  
Kang Liu ◽  
Jian Zheng Cheng ◽  
Li Cheng
Keyword(s):  
White Noise ◽  
Speech Enhancement ◽  
Speech Signal ◽  
Bayesian Framework ◽  
Threshold Function ◽  
Wavelet Thresholding ◽  
Wavelet Coefficients ◽  
Edge Preservation ◽  
Adaptive Wavelet ◽  
Locally Adaptive

There are strong dependencies between wavelet coefficients of speech signal,in this article,based on that,a new corresponding nonlinear threshold function derived in Bayesian framework is proposed to decrease the effect of the ambient noise.Analysis of the data shows the effectiveness of the proposed method that it removes white noise more effectually and gets better edge preservation.

scholarly journals Performance on Speech Enhancement Objective Quality Measures Using Hybrid Wavelet Thresholding

2019 ◽  
Vol 8 (6) ◽  
pp. 3523-3533
Keyword(s):  
Speech Enhancement ◽  
Speech Signal ◽  
Quality Measures ◽  
Wavelet Thresholding ◽  
Wavelet Coefficients ◽  
Hard Thresholding ◽  
Improvement Set ◽  
Improvement Sets ◽  
Objective Quality

The quality of being easily understandable of the spe ech signals are very importantin communication and other speec h related systems. In order to improve these two in thespeech sign al, Speech improvement sets of computer instructions and devices are used so that itmay be better fully used by other speech proces sing setsof computer instructions. Most of the speech communica tion that requires atleast one microphone and the desired speech signal is usually contaminated by backgroundnoise and echo. As a result, the speech sign must be "cleaned" with advanced sign preparing devices before it is played out, transmitted, or put away. In this venture it has been investigated the required things and degree of upgrades in the field of discourse improvement utilizing discourse de-noising sets of PC directions announced in books with the fundamental intend to concentrate on the utilization of the window shape limits/rules in STSA based Speech Improvement process in which the sign destroyed by commotion is into edges and each part/segment is Windowed and the Windowed Speech pieces/parts zone connected to the Speech Improvement set of PC guidelines and the Improved Speech sign is modified in its time area. In general, the Speech Improvement methods make use theHam ming Window for this purpose. In this work an attempt has been made to study the effect of Window shape on the Speech. The Modified Improved thresholding is proposed by Asser Ghanbari and Mohammad Reza Karami and can be used like a hard thresholding limit with respect to the wavelet coefficients through and through worth progressively conspicuous than limit esteem and resembles an exponential capacity for the wavelet coefficients supreme worth not as much as edge esteem and is characterized.

Improved Wavelet Thresholding for CCD Measurement Image Denoising

2014 ◽  
Vol 574 ◽  
pp. 432-435 ◽  
Author(s):  
Jie Zhan ◽  
Zhen Xing Li
Keyword(s):  
Image Denoising ◽  
Threshold Function ◽  
Wavelet Thresholding ◽  
Wavelet Coefficients ◽  
Local Correlation ◽  
Thresholding Method ◽  
Threshold Functions ◽  
Soft Threshold ◽  
Simulation Results ◽  
Filtering Effect

An improved wavelet thresholding method is presented and successfully applied to CCD measuring image denoising. On the analysis of the current widely used soft threshold and hard threshold, combining characteristics of the CCD measuring image and use of local correlation of wavelet coefficients, an improved threshold function is proposed, and the denoising results were contrasted among different threshold functions. The simulation results show that adopting the improved threshold function can acquire better filtering effect than traditional soft threshold and hard threshold methods.

A variant of SWEMDH technique based on variational mode decomposition for speech enhancement

2021 ◽  
Vol 25 (3) ◽  
pp. 299-308
Author(s):  
Poovarasan Selvaraj ◽  
E. Chandra
Keyword(s):  
White Noise ◽  
Speech Enhancement ◽  
Speech Signal ◽  
High Frequency ◽  
Hurst Exponent ◽  
Sliding Window ◽  
Variational Mode Decomposition ◽  
Vocal Signal ◽  
Mode Decomposition ◽  
Vocal Signals

In Speech Enhancement (SE) techniques, the major challenging task is to suppress non-stationary noises including white noise in real-time application scenarios. Many techniques have been developed for enhancing the vocal signals; however, those were not effective for suppressing non-stationary noises very well. Also, those have high time and resource consumption. As a result, Sliding Window Empirical Mode Decomposition and Hurst (SWEMDH)-based SE method where the speech signal was decomposed into Intrinsic Mode Functions (IMFs) based on the sliding window and the noise factor in each IMF was chosen based on the Hurst exponent data. Also, the least corrupted IMFs were utilized to restore the vocal signal. However, this technique was not suitable for white noise scenarios. Therefore in this paper, a Variant of Variational Mode Decomposition (VVMD) with SWEMDH technique is proposed to reduce the complexity in real-time applications. The key objective of this proposed SWEMD-VVMDH technique is to decide the IMFs based on Hurst exponent and then apply the VVMD technique to suppress both low- and high-frequency noisy factors from the vocal signals. Originally, the noisy vocal signal is decomposed into many IMFs using SWEMDH technique. Then, Hurst exponent is computed to decide the IMFs with low-frequency noisy factors and Narrow-Band Components (NBC) is computed to decide the IMFs with high-frequency noisy factors. Moreover, VVMD is applied on the addition of all chosen IMF to remove both low- and high-frequency noisy factors. Thus, the speech signal quality is improved under non-stationary noises including additive white Gaussian noise. Finally, the experimental outcomes demonstrate the significant speech signal improvement under both non-stationary and white noise surroundings.

scholarly journals Speech signal enhancement through adaptive wavelet thresholding

2007 ◽  
Vol 49 (2) ◽  
pp. 123-133 ◽  
Author(s):  
Michael T. Johnson ◽  
Xiaolong Yuan ◽  
Yao Ren
Keyword(s):  
Speech Signal ◽  
Signal Enhancement ◽  
Wavelet Thresholding ◽  
Adaptive Wavelet

Adaptive wavelet filtering for bearing monitoring based on block bootstrapping and white noise test

2011 ◽  
Vol 226 (9) ◽  
pp. 2345-2360 ◽  
Author(s):  
Du Wenliao ◽  
Yuan Jin ◽  
Li Yanming ◽  
Liu Chengliang
Keyword(s):  
White Noise ◽  
Wavelet Coefficient ◽  
Vibration Signal ◽  
Wavelet Domain ◽  
Wavelet Coefficients ◽  
Decomposition Level ◽  
Wavelet Filtering ◽  
Adaptive Wavelet ◽  
Noise Test ◽  
Bearing Monitoring

This study describes a novel scheme of adaptive wavelet filtering for bearing monitoring based on block bootstrapping and white noise test. The scheme consists of three main steps. First, the vibration signal is decomposed into wavelet domain, and the correlations between the wavelet coefficients are measured by lag autocorrelations. Second, according to the intensity of correlation at each level, either the block bootstrapping or general bootstrapping procedure is adopted to produce new pseudo-samples from the original wavelet coefficient series. Finally, as actual signal and noise have different translating characters along the levels in wavelet domain, the optimal decomposition level is achieved through whitening test on the wavelet coefficients, and the accuracy of the test is also obtained by the pseudo-samples. The simulation and experimental results show that the proposed procedure can be used to adaptively determine the optimal decomposition level and obtain superior filtering capability.

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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