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).

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).

Singular Values Decomposition and Lifting Wavelet Transform for Speech Signal Embedding into Digital Image

2019 ◽  
Vol 12 (2) ◽  
pp. 138-151
Author(s):  
Mourad Talbi ◽  
Med Salim Bouhlel
Keyword(s):  
Wavelet Transform ◽  
Speech Signal ◽  
Signal To Noise Ratio ◽  
Perceptual Quality ◽  
Lifting Wavelet Transform ◽  
Signal To Noise ◽  
Perceptual Evaluation ◽  
Lifting Wavelet ◽  
Noise Ratio

Background: In this paper, we propose a secure image watermarking technique which is applied to grayscale and color images. It consists in applying the SVD (Singular Value Decomposition) in the Lifting Wavelet Transform domain for embedding a speech image (the watermark) into the host image. Methods: It also uses signature in the embedding and extraction steps. Its performance is justified by the computation of PSNR (Pick Signal to Noise Ratio), SSIM (Structural Similarity), SNR (Signal to Noise Ratio), SegSNR (Segmental SNR) and PESQ (Perceptual Evaluation Speech Quality). Results: The PSNR and SSIM are used for evaluating the perceptual quality of the watermarked image compared to the original image. The SNR, SegSNR and PESQ are used for evaluating the perceptual quality of the reconstructed or extracted speech signal compared to the original speech signal. Conclusion: The Results obtained from computation of PSNR, SSIM, SNR, SegSNR and PESQ show the performance of the proposed technique.

scholarly journals Dual-Channel Speech Enhancement Based on Speech Presence Probability

2020 ◽  
Vol 9 (3) ◽  
pp. 2198-2200
Keyword(s):  
Noise Reduction ◽  
Speech Enhancement ◽  
Hearing Aids ◽  
Speech Processing ◽  
Speech Signal ◽  
Reduction Process ◽  
Coherent Noise ◽  
Residual Noise ◽  
Speech Distortion ◽  
Reduction Methods

This paper introduces technology to improve sound quality, which serves the needs of media and entertainment. Major challenging problem in the speech processing applications like mobile phones, hands-free phones, car communication, teleconference systems, hearing aids, voice coders, automatic speech recognition and forensics etc., is to eliminate the background noise. Speech enhancement algorithms are widely used for these applications in order to remove the noise from degraded speech in the noisy environment. Hence, the conventional noise reduction methods introduce more residual noise and speech distortion. So, it has been found that the noise reduction process is more effective to improve the speech quality but it affects the intelligibility of the clean speech signal. In this paper, we introduce a new model of coherence-based noise reduction method for the complex noise environment in which a target speech coexists with a coherent noise around. From the coherence model, the information of speech presence probability is added to better track noise variation accurately; and during the speech presence and speech absent period, adaptive coherence-based method is adjusted. The performance of suggested method is evaluated in condition of diffuse and real street noise, and it improves the speech signal quality less speech distortion and residual noise.

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 An Improved MMSE Amplitude Estimator under Generalized Gamma Distribution Based on Auditory Perception

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Chabane Boubakir ◽  
Daoud Berkani
Keyword(s):  
Gamma Distribution ◽  
Speech Enhancement ◽  
Minimum Mean Square Error ◽  
Masking Effect ◽  
Spectral Amplitude ◽  
Auditory Masking ◽  
Generalized Gamma Distribution ◽  
Generalized Gamma ◽  
Mmse Estimator ◽  
Short Time

This paper describes a new speech enhancement approach which employs the minimum mean square error (MMSE) estimator based on the generalized gamma distribution of the short-time spectral amplitude (STSA) of a speech signal. In the proposed approach, the human perceptual auditory masking effect is incorporated into the speech enhancement system. The algorithm is based on a criterion by which the audible noise may be masked rather than being attenuated, thereby reducing the chance of speech distortion. Performance assessment is given to show that our proposal can achieve a more significant noise reduction as compared to the perceptual modification of Wiener filtering and the gamma based MMSE estimator.

scholarly journals Multiresolution Cochleagram Speech Enhancement Algorithm Using Improved Deep Neural Networks with Skip Connections

2021 ◽  
Author(s):  
chaofeng lan ◽  
Chundong Liu ◽  
Lei Zhang
Keyword(s):  
Speech Enhancement ◽  
Loss Function ◽  
Minimum Mean Square Error ◽  
Spectral Amplitude ◽  
Enhancement Effect ◽  
Low Snr ◽  
Input Feature ◽  
Four Levels ◽  
Short Time ◽  
Better Than

Abstract Deep learning based methods have been a recent benchmark method for speech enhancement. However, these approaches are limited in low signal-to-noise ratios (SNR) conditions, for speech loss and low intelligibility. To address this problem, we improve Multi-Resolution Cochleagram (MRCG), and gammachirp filter bank is used to decompose the speech signal in time and frequency, and the low-resolution signal is denoised by the minimum mean-square error short-time spectral amplitude estimator (MMSE-STSA). Improve Multi-Resolution Cochleagram (I-MRCG) is adopted as the input feature of Skip connections-DNN (Skip-DNN). In this paper, the source to distortion ratio (SDR) is used in the training process, and the logarithm is introduced to observe the iterative process more clearly. Experiments were performed on the TIMIT database with four noise types at four levels of SNR. I-MRCG as the input feature of the Skip-DNN model, the average PESQ is 2.6783, and the average STOI is 0.8752. Compared with MRCG, the PESQ and STOI obtained by MRCG are increased 1.4% and 1.5%, respectively. This shows that MRCG is the input feature of the Skip-DNN model, and the speech enhancement effect after training is better than other features. It can not only solve the problem of speech loss in a low SNR environment, but also obtain more robust speech enhancement. The loss function experiment shows that compared to MSE and SDR, the improved SDR as the loss function of the speech enhancement model has the best enhancement effect.

Speech enhancement algorithm of improved OMLSA based on bilateral spectrogram filtering

2020 ◽  
Vol 39 (5) ◽  
pp. 6881-6889
Author(s):  
Jie Wang ◽  
Linhuang Yan ◽  
Jiayi Tian ◽  
Minmin Yuan
Keyword(s):  
Speech Enhancement ◽  
Visual Processing ◽  
Single Channel ◽  
Signal To Noise Ratio ◽  
Spectral Amplitude ◽  
Signal To Noise ◽  
Noisy Speech ◽  
Time Frequency ◽  
Perceptual Evaluation ◽  
Noise Ratio

In this paper, a bilateral spectrogram filtering (BSF)-based optimally modified log-spectral amplitude (OMLSA) estimator for single-channel speech enhancement is proposed, which can significantly improve the performance of OMLSA, especially in highly non-stationary noise environments, by taking advantage of bilateral filtering (BF), a widely used technology in image and visual processing, to preprocess the spectrogram of the noisy speech. BSF is capable of not only sharpening details, removing unwanted textures or background noise from the noisy speech spectrogram, but also preserving edges when considering a speech spectrogram as an image. The a posteriori signal-to-noise ratio (SNR) of OMLSA algorithm is estimated after applying BSF to the noisy speech. Besides, in order to reduce computing costs, a fast and accurate BF is adopted to reduce the algorithm complexity O(1) for each time-frequency bin. Finally, the proposed algorithm is compared with the original OMLSA and other classic denoising methods using various types of noise with different signal-to-noise ratios in terms of objective evaluation metrics such as segmental signal-to-noise ratio improvement and perceptual evaluation of speech quality. The results show the validity of the improved BSF-based OMLSA algorithm.

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