Rehaussement du signal de parole par EMD et opérateur de Teager-Kaiser

The authors would like to thank Professor Mohamed Bahoura from Universite de Quebec a Rimouski for fruitful discussions on time adaptive thresholdingIn this paper a speech denoising strategy based on time adaptive thresholding of intrinsic modes functions (IMFs) of the signal, extracted by empirical mode decomposition (EMD), is introduced. The denoised signal is reconstructed by the superposition of its adaptive thresholded IMFs. Adaptive thresholds are estimated using the Teager–Kaiser energy operator (TKEO) of signal IMFs. More precisely, TKEO identifies the type of frame by expanding differences between speech and non-speech frames in each IMF. Based on the EMD, the proposed speech denoising scheme is a fully data-driven approach. The method is tested on speech signals with different noise levels and the results are compared to EMD-shrinkage and wavelet transform (WT) coupled with TKEO. Speech enhancement performance is evaluated using output signal to noise ratio (SNR) and perceptual evaluation of speech quality (PESQ) measure. Based on the analyzed speech signals, the proposed enhancement scheme performs better than WT-TKEO and EMD-shrinkage approaches in terms of output SNR and PESQ. The noise is greatly reduced using time-adaptive thresholding than universal thresholding. The study is limited to signals corrupted by additive white Gaussian noise

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

A novel image enhancement method for mammogram images

Breast cancer has been reported by American Cancer Society as the second leading cause of death among all the cancers of women. It is also reported that the early detection of breast cancer can improve survival rate by allowing a wider range of treatment options. Mammography is believed to be an effective tool to help radiologists to detect the malignant breast cancer at the early stage. Image enhancement techniques can improve the quality of mammogram images with enhancing the details of key features, like the shape of microcalcifications. This thesis proposed a novel method to enhance mammogram images. The proposed method uses a three level Laplacian Pyramid (LP) scheme that applies the Squeeze Box Filter (SBF) instead of conventional low pass filtering. A previously proposed nonlinear local enhancement technique is applied to the difference image produced in the Laplacian Pyramid to contrast enhance the structural details of mammogram images. The enhanced mammogram image is reconstructed by adding all the enhanced difference images to the origianl SBF filtered image. Experimentation and quantitative results reported in this thesis provide empirical evidence on the robustness of the proposed image enhancement method on mammographic images

Uji Kinerja Sistem Denoising Sinyal Jantung atau EKG dengan Menggunakan Algoritma Empirical Mode Decomposition (EMD)

Electrocardiograms (EKGs) are signals created by the electrical activity of the heart muscle and displayed on the EKG device's monitor. Using the EKG recording, the primary characteristics for diagnosing the status of the human heart can be determined. The death rate of heart patients can be reduced through early identification of cardiac problems. In ECG readings, it is frequently affected by several disruptions caused by muscle contractions and electrode movement. Numerous investigations on ECG signal denoising techniques have been undertaken earlier. This article examines the testing of the performance of EKGs using the denoising technique based on the Empirical Mode Decomposition (EMD) algorithm. In this work, many metrics were utilized to evaluate the ECG signal denoising technique: mean square error (MSE), mean absolute error (MAE), and signal-to-noise ratio (SNR). In this investigation, the ECG data was contaminated with noise from muscle artifacts (MA), additive Gaussian white noise (AWGN), electrode movement (EM), and baseline wander (BW). The noise-contaminated ECG signal is subsequently subjected to the denoising process. Calculate the MSE, MAE, and SNR values of the signal after it has been denoised. This study includes a scenario for testing three thresholding techniques with four distinct types of noise. The performance of the hard thresholding method is superior for all types of noise. MSE is produced by AWGN, which is 0.15, 0.28, and 9.9 dB. MA noise generates MSE, MAE, and SNR values of 0.4, 0.033, and 41.0 dB, respectively. The EM noise has an MSE of 0.010, an MAE of 0.04, and an SNR of 30.8 dB. The MSE produced by BW noise is 0.008; the MAE and SNR values were 0.0356 and 28.5, respectively

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

Adaptive wavelet thresholding with robust hybrid features for text-independent speaker identification system

The robustness of speaker identification system over additive noise channel is crucial for real-world applications. In speaker identification (SID) systems, the extracted features from each speech frame are an essential factor for building a reliable identification system. For clean environments, the identification system works well; in noisy environments, there is an additive noise, which is affect the system. To eliminate the problem of additive noise and to achieve a high accuracy in speaker identification system a proposed algorithm for feature extraction based on speech enhancement and a combined features is presents. In this paper, a wavelet thresholding pre-processing stage, and feature warping (FW) techniques are used with two combined features named power normalized cepstral coefficients (PNCC) and gammatone frequency cepstral coefficients (GFCC) to improve the identification system robustness against different types of additive noises. Universal Background Model Gaussian Mixture Model (UBM-GMM) is used for features matching between the claim and actual speakers. The results showed performance improvement for the proposed feature extraction algorithm of identification system comparing with conventional features over most types of noises and different SNR ratios

A Combined Model for Noise Reduction of Lung Sound Signals Based on Empirical Mode Decomposition and Artificial Neural Network

Computer analysis of Lung Sound (LS) signals has been proposed in recent years as a tool to analyze the lungs' status but there have always been main challenges, including the contamination of LS with environmental noises, which come from different sources of unlike intensities. One of the common methods in noise reduction of LS signals is based on thresholding on Discrete Wavelet Transform (DWT) coefficients or Empirical Mode Decomposition (EMD) of the signal, however, in these methods, it is necessary to calculate the SNR value to determine the appropriate threshold for noise removal. To solve this problem, a combined model based on EMD and Artificial Neural Network (ANN) trained with different SNRs (0, 5, 10, 15, and 20dB) is proposed in this research. The model can denoise white and pink noises in the range of -2 to 20dB without thresholding or even estimating SNR, and at the same time, keep the main content of the LS signal well. The proposed method is also compared with the EMD-custom method, and the results obtained from the SNR, and fit criteria indicate the absolute superiority of the proposed method. For example, at SNR = 0dB, the combined method can improve the SNR by 9.41 and 8.23dB for white and pink noises, respectively, while the corresponding values are respectively 5.89 and 4.31dB for the EMD-Custom method