Wavelet speech enhancement based on time-scale adaptation

Abstract : We propose a new speech enhancement method based on time and scale adaptation of wavelet thresholds. The time dependency is introduced by approximating the Teager Energy of the wavelet coefficients, while the scale dependency is introduced by extending the principle of level dependent threshold to Wavelet Packet Thresholding. This technique does not require an explicit estimation of the noise level or of the apriori knowledge of the SNR, as is usually needed in most of the popular enhancement methods. Performance of the proposed method is evaluated on speech recorded in real conditions (plane, sawmill, tank, subway, babble, car, exhibition hall, restaurant, street, airport, and train station) and artificially added noise. MELscale decomposition based on wavelet packets is also compared to the common wavelet packet scale. Comparison in terms of Signal-to-Noise Ratio (SNR) is reported for time adaptation and time-scale adaptation thresholding of the wavelet coefficients thresholding. Visual inspection of spectrograms and listening experiments are also used to support the results. Hidden Markov Models Speech recognition experiments are conducted on the AURORA–2 database and show that the proposed method improves the speech recognition rates for low SNRs

Wavelet Packet Transform based Speech Enhancement via Two-Dimensional SPP Estimator with Generalized Gamma Priors

Despite various speech enhancement techniques have been developed for different applications, existing methods are limited in noisy environments with high ambient noise levels. Speech presence probability (SPP) estimation is a speech enhancement technique to reduce speech distortions, especially in low signal-to-noise ratios (SNRs) scenario. In this paper, we propose a new two-dimensional (2D) Teager-energyoperators (TEOs) improved SPP estimator for speech enhancement in time-frequency (T-F) domain. Wavelet packet transform (WPT) as a multiband decomposition technique is used to concentrate the energy distribution of speech components. A minimum mean-square error (MMSE) estimator is obtained based on the generalized gamma distribution speech model in WPT domain. In addition, the speech samples corrupted by environment and occupational noises (i.e., machine shop, factory and station) at different input SNRs are used to validate the proposed algorithm. Results suggest that the proposed method achieves a significant enhancement on perceptual quality, compared with four conventional speech enhancement algorithms (i.e., MMSE-84, MMSE-04, Wiener-96, and BTW)

A New Wavelet Denoising Method for Noise Threshold

A new method is used wavelet 1-D experimental signal for denoising. It is provided the optimal adaptive threshold of sub-band based on input signals. The new method: 1) use a new method with low complexity that calculates thresholds; 2) use threshold for each sub-bands; 3) divide three sub-band with range of human hearing and range of the hearing tests are often displayed in the form of an audiogram; 4) use a new denoising algorithm depends on attribute of signal for wavelet coefficients; 5) applies denoising to the detail coefficients. The new method called Adaptive Thresholding with Mean for hybrid Denoising method of hard and soft function (ATMDe) and applied to hearing loss and it is found that it increases the signal-to-noise ratio by more than 114 % and decreases the mean-square-error (MSE). The result of new method with SNR and MSE is higher than standard denoising methods. Hence, the new method was found that has good performance and adaptive threshold value is better than other methods.This study is proposed a new adaptive threshold based on noisy speech for each sub-bands with low complex and it is suitability for range of human hearing and range of hearing test. A new method is used wavelet 1-D experimental signal for denoising. It provided the optimal adaptive threshold of three sub-band with applies to the detail coefficients. The speech enhancement is used of threshoding on the adpated wavelet coefficients, and the results are compared a variety of noisy speech and four well-known benchmark signals. The results, measured objectively by Signal-to-Noise ratio (SNR) and Mean Square Error (MSE), are given for additive white Gaussian noise as well as two different types of noisy environment. The new method called Adaptive Thresholding with Mean for hybrid Denoising method of hard and soft function (ATMDe) and applied to hearing loss and it is found that it increases the signal-to-noise ratio by more than 114% and decreases the mean-square-error (MSE). The result of new method with SNR and MSE is higher than standard denoising methods. Hence, the new method was found that has good performance and adaptive threshold value is better than other methods

A New Speech Enhancment algorithm in Hearing Aid based on Wavelet Transform

Voice Enhancement systems are used to remove background inference in a speech signal and are become an main component of modern hearing aid. In everyday life the speech communication is vivid uses for the hearing impaired and the numerous other applications. Speech is the fundamental means of human communication. After over thirty years of research enhancement algorithm. Which offer superior noise reduction over current methods? All speech enhancements suffer from distortion for the residual noise due to imperfect noise removal. It is always require to perform denoising in voice processing system operating in highly noise because of wavelet transform is one of the popular techniques used in signal enhancement, In the present paper wavelet thresholding and wavelet packet thresholding method have been used to decrease the noise from the voice signal. A simple threshold method is presented to compute the optimum threshold value. Mean square error(MSE) at different values of SNR is computed to method like traditional speech subtraction, wiener filtering method, spectral subtraction with MMSE etc.The result obtained is compared with the other voice enhancement algorithm given in various reference papers. In comparison to other traditional methods we get improved result in terms of SNR and MSE.Simulation done in MATLAB platform

Speech Enhancement via EMD

WOSInternational audienceIn this study, two new approaches for speech signal noise reduction based on the empirical mode decomposition (EMD) recently introduced by Huang et al. (1998) are proposed. Based on the EMD, both reduction schemes are fully data-driven approaches. Noisy signal is decomposed adaptively into oscillatory components called intrinsic mode functions (IMFs), using a temporal decomposition called sifting process. Two strategies for noise reduction are proposed: filtering and thresholding. The basic principle of these two methods is the signal reconstruction with IMFs previously filtered, using the minimum mean-squared error (MMSE) filter introduced by I. Y. Soon et al. (1998), or thresholded using a shrinkage function. The performance of these methods is analyzed and compared with those of the MMSE filter and wavelet shrinkage. The study is limited to signals corrupted by additive white Gaussian noise. The obtained results show that the proposed denoising schemes perform better than the MMSE filter and wavelet approach