Signal Denoising Research Articles

Robust Frequency Domain Spline Adaptive Filtering Based on the Half-Quadratic Criterion: Performance Analysis and Applications

Frequency domain spline adaptive filters (FDSAF) have attracted a great deal of attention because of their enhanced computational efficiency. However, their performance will be extremely degraded in practical environments where non-Gaussian phenomena exist. In order to attain good robustness to non-Gaussian noises while ensuring a low computational burden, this paper proposes a frequency domain spline adaptive filtering algorithm based on the half-quadratic criterion (HQC), named FDSAF-HQC. Further, to offer a theoretical foundation for the proposed FDSAF-HQC, a detailed performance analysis based on the principle of energy conservation is conducted and then validated by simulations. Moreover, the computational complexity of FDSAF-HQC is evaluated and compared with other similar algorithms. Simulation results of nonlinear system identification corroborate the outstanding performance of the proposed FDSAF-HQC, i.e., better robustness and computational efficiency. The superiority of the proposed FDSAF-HQC is also supported by practical applications of echo cancellation and biological signal denoising.

An Automated GPR Signal Denoising Scheme Based on Mode Decomposition and Principal Component Analysis

Ground penetrating radar (GPR) signal processing has used mode decomposition methods such as empirical mode decomposition (EMD), variational mode decomposition, and their variations, but these methods are typically constrained by manual mode selection for the extraction of real signals and unwanted noises. In this study, we propose an automatic mode selection scheme for GPR signal denoising, which is demonstrated by the combination of the improved complete ensemble EMD (ICEEMD) and the multiscale principal component analysis (MSPCA). For GPR B-scan data, the intrinsic mode functions (IMFs) are automatically screened using our predefined criteria to remove noise after their spectra and the relationship between signals are first analyzed. Next, the nonlinear and nonstationary signals are adaptively decomposed into many time series by the proposed automated ICEEMD. Each A-scan dataset is then screened and subjected to MSPCA before being reassembled into the final denoised result. The synthetic and real GPR data prove our denoising scheme’s superiority over more established denoising techniques like MSPCA and median filtering.

DBSA-Net: Dual Branch Self-Attention Network for Underwater Acoustic Signal Denoising

Underwater acoustic signal denoising is a challenging task due to the complexity of the underwater environment. Most of the existing methods cannot effectively cope with the problem of underwater acoustic signal (UWAS) denoising at low signal-to-noise ratios (SNRs). According to the characteristics of UWAS, a novel idea is proposed to simultaneously model latent features from both the time and frequency dimensions of complex-valued spectrum in a dual-branch self-attention network, namely DBSA-Net. In this model, both magnitude and phase information in the complex spectrum are enhanced from different dimensions by two branches. Specifically, DBSA-Net is an encoder-decoder based network with several global-local-self-attention (GL-SA) blocks distributed on dual branches between encoder and decoder. Each GL-SA block incorporates global self-attention and local self-attention to capture distant context and fine-grained local dependencies along the temporal and frequency dimensions. Moreover, we also design an information interaction module between two branches to exchange complementary information. This interaction module together with a merge block fuse features extracted from different dimensions, thus enhancing the capability of our model to learn the target signal features. Extensive experiments are conducted to evaluate our model on a publicly available dataset. Results of the ablation experiments show that the different modules of DBSA-Net play their respective roles in improving denoising performance and are empirically valid. In both the seen ships and unseen ships scenarios, the proposed DBSA-Net outperforms existing approaches by a large margin on various evaluation metrics.

New Fractional Wavelet with Compact Support and Its Application to Signal Denoising

New Fractional Wavelet with Compact Support and Its Application to Signal Denoising

A Novel Noise-Aware Deep Learning Model for Underwater Acoustic Denoising

Underwater acoustic signal denoising technology aims to overcome the challenge of recovering valuable ship target signals from noisy audios by suppressing underwater background noise. Traditional statistical-based denoising techniques are difficult to be applied effectively in complex underwater environments, especially in the case of extremely low signal-to-noise ratios (SNRs). To address these problems, we propose a noise-aware deep learning model with fullband-subband attention network (NAFSA-Net) for underwater acoustic signal denoising. NAFSA-Net adopts an encoder to extract the feature representation of the input audio. Subsequently, the noise subnet and the target subnet are designed to estimate the noise component and the target component simultaneously. Specifically, some stacked fullband-subband attention (FSA) blocks are deployed in each subnet to capture both global dependencies and fine-grained local dependencies of features. Furthermore, we introduce an interaction module to transmit auxiliary information from the noise subnet to the target subnet. Finally, we propose an improved weight SI-SNR loss function to optimize the training of our model. Experimental results show that our proposed NAFSA-Net substantially outperforms traditional methods and competitive DNN-based solutions in denoising underwater noisy signals with very low SNRs. More importantly, our proposals achieve equally excellent performance on both unseen datasets, which indicates that NAFSA-Net can be a more robust choice for real-world underwater acoustic denoising systems.

Performance Analysis of FIR Adaptive Filter Algorithms for Denoising Adult ECG Signals: A Comparative Study

This paper describes a unique study that uses multiple FIR adaptive filter algorithms to denoise adult electrocardiogram (ECG) data. The study looks at how power line interference, external electromagnetic fields, random body motions, and breathing impact ECG measurement accuracy. The article takes a fresh look at Savitzky-Golay filtering techniques by implementing and evaluating them inside the FIR adaptive filter architecture. Matlab is used to evaluate the performance of the Affine projection FIR adaptive filter (AP), Direct-form Normalized least-mean-square FIR adaptive filter (NLMS), and Sliding-window Recursive least-squares FIR adaptive filter (SWRLS). The results show how different strategies compare in terms of performance and their influence on recorded waveform quality. The study extends to our understanding of the efficiency of FIR adaptive filter algorithms in decreasing ECG signal noise and helps us better understand their potential uses in ECG signal processing. Based on reliable ECG data, the research findings assist the development of new approaches for diagnosing aberrant cardiac rhythms and examining the origins of chest discomfort. The originality of this work comes in its thorough assessment, comparison, and unique use of Savitzky-Golay filtering techniques inside FIR adaptive filter algorithms, which contributes to the area of ECG signal denoising. According to a comparative investigation, the SWRLS FIR adaptive filter method improves ECG signal denoising by 91.53% noise reduction.

A survey on encoding and decoding technology of non-invasive brain-computer interface

A survey on encoding and decoding technology of non-invasive brain-computer interface

A Review on the Applications of Time‐Frequency Methods in ECG Analysis

The joint time‐frequency analysis method represents a signal in both time and frequency. Thus, it provides more information compared to other one‐dimensional methods. Several researchers recently used time‐frequency methods such as the wavelet transform, short‐time Fourier transform, empirical mode decomposition and reported impressive results in various electrophysiological studies. The current review provides comprehensive knowledge about different time‐frequency methods and their applications in various ECG‐based analyses. Typical applications include ECG signal denoising, arrhythmia detection, sleep apnea detection, biometric identification, emotion detection, and driver drowsiness detection. The paper also discusses the limitations of these methods. The review will form a reference for future researchers willing to conduct research in the same field.

Application of Variational Mode Decomposition and Whale Optimization Algorithm to Laser Ultrasonic Signal Denoising

Laser ultrasound signal echoes are easily drowned out by the surrounding environmental noise in industrial field applications, and it is worthwhile to study methods of retaining the weak ultrasound signal during signal processing. To address this problem, this paper proposes to adopt the parameters optimized by the whale optimization algorithm to the variational mode decomposition (VMD) of laser ultrasound signals. The optimized parameters can avoid the frequency mixing and incomplete noise separation caused by the choice of artificial VMD parameters. The Hausdorff distance is applied in the process of reconstructing the signal to help accurately select the relevant modes and improve the signal-to-noise ratio. Simulation and experimental results show that the proposed method is feasible and effective compared with the other three available denoising methods.

Study on Denoising Microseismic Signal Based on Autoencoder Convolutional Neural Networks

As a kind of dynamic real-time monitoring technology, microseismic monitoring technology has been widely used for rockburst warning. Due to the complexity of the actual monitoring environment, the monitoring signals often contain different types of noise, affecting the earning warning of rockburst. In this study, an Autoencoder Convolutional Neural Network denoising model based on deep learning has been proposed to denoising of the complex signals. The unsupervised adaptive training method is used to train the model, which only needs to set its initial parameters. The importance of an enhanced training dataset is illustrated by the comparison experiment. The results indicate that the training and verification shows well performance during training. The denoising efficiency of the proposed model is studied by the denoising of the synthetic noise-containing signals. Furthermore, the dataset from the water conveyance tunnel in the Hanjiang-to-Weihe River water diversion project (HJ-Project) in Shaanxi Province is taken as an engineering example to evolute the performance of the proposed model for practical project. The denoising performance of the model is analysed through the visual denoising results and evaluation index. The model can effectively denoise the complex noised signal which separate it into pure microseismic signal and noise signal, and improve the signal-to-noise ratio, which is benefit for arrive-time picking and source locating then improve the performance of early warning of rockburst.

Research on noise reduction method for ship radiate noise based on secondary decomposition

Underwater acoustic signal denoising is of great significance for the accurate identification of underwater targets, the research of military detection equipment and the monitoring of marine ecological environment. In this paper, a new denoising method for ship radiated noise based on combined secondary optimization decomposition model, amplitude-aware permutation entropy, dynamic interval threshold filtering and mutual information is proposed. Compared with one-time decomposition, the proposed method can better restore the waveform characteristics of the original signal and filter out the mixed residual noise. In addition, this paper discoveries the influence of differential operation on phase diagram morphology under high signal-to-noise ratio. The experimental results show that the differential phase diagram can show the weak noise contained in the signal with high signal-to-noise ratio. This new discovery has been applied to the evaluation of noise reduction effect of underwater acoustic signal for the first time.

Screening of Discrete Wavelet Transform Parameters for the Denoising of Rolling Bearing Signals in Presence of Localised Defects

Maintenance scheduling is a fundamental element in industry, where excessive downtime can lead to considerable economic losses. Active monitoring systems of various components are ever more used, and rolling bearings can be identified as one of the primary causes of failure on production lines. Vibration signals extracted from bearings are affected by noise, which can make their nature unclear and the extraction and classification of features difficult. In recent years, the use of the discrete wavelet transform for denoising has been increasing, but studies in the literature that optimise all the parameters used in this process are lacking. In the current article, the authors present an algorithm to optimise the parameters required for denoising based on the discrete wavelet transform and thresholding. One-hundred sixty different configurations of the mother wavelet, threshold evaluation method, and threshold function are compared on the Case Western Reserve University database to obtain the best combination for bearing damage identification with an iterative method and are evaluated with tradeoff and kurtosis. The analysis results show that the best combination of parameters for denoising is dmey, rigrSURE, and the hard threshold. The signals were then distributed in a 2D plane for classification through an algorithm based on principal component analysis, which uses a preselection of features extracted in the time domain.

De-noising of partial discharge ultrasonic signal of insulation bar in large motor based on GMC-wavelet

Abstract In view of the bad operation environment of large motor, which often suffers from various strong noise interference, the partial discharge ultrasonic signal is often annihilated, which makes it difficult to detect and analyse. A de-noising method based on generalized minimax concavity (GMC) and wavelet for partial discharge (PD) ultrasonic signal is proposed. GMC is used to enhance the sparsity of PD ultrasonic signal and eliminate the high-frequency noise signal at the same time. Then the residual high-frequency sparse noise and low-frequency noise of the former are de-noised again combined with wavelet. Finally, the signal is reconstructed to achieve the purpose of de-noising the original PD ultrasonic signal with noise. Compared with ℓ1 -norm method, GMC method, wavelet method and ℓ1 -norm-wavelet method, the simulation results show that based on time domain analysis, the de-noising effect of the proposed method is obviously better than the other four methods. The SNR and MSE of the former are better than those of the latter. In addition, the insulation bar discharge model of large motor is constructed to obtain the actual PD ultrasonic signal, which further verifies its effectiveness, and its de-noising effect is also better than the four methods. This method can not only enhance the sparsity of the target signal and improve the estimation accuracy, but also achieve the de-noising effect, while retaining the effective information of PD ultrasonic signal characteristics. This method can provide new ideas for other types of PD signal de-noising, and lay the foundation for later feature analysis.

Lower limb motion recognition based on surface electromyography

Because the noise in lower limb surface electromyography (sEMG) is so high, extracting characteristics and recognizing lower limb motion is difficult. This research offers an improved threshold algorithm based on parameter α for sEMG signal denoising, convolution neural network (CNN) feature extraction, and kernel extreme learning machine (KELM) classification of lower limb motion recognition to address these issues. First, the sEMG signal decomposes into multiple variational mode functions (VMF) by variational mode decomposition, and the denoised sEMG signal is reconstructed after the VMF is processed by the enhanced threshold algorithm. The denoised sEMG signal is then sent into the CNN network as a matrix to achieve independent feature extraction of the sEMG signal. Finally, KELM is used to classify the retrieved feature signal. The experimental results reveal that this approach has a high signal-to-noise ratio, the root mean square is small, and a good denoising impact. The accuracy of lower limb classification is 95.90%, Based on traditional features and CNN, it is 18.29% and 11.86% higher than KELM, respectively. It can be seen that this research method can improve the accuracy of lower limb motion recognition.

The Atmospheric Vertical Detection of Large Area Regions Based on Interference Signal Denoising of Weighted Adaptive Kalman Filter.

In comparison with traditional space infrared spectroscopy technology, the interference signals of a large focal plane array (FPA) can be used to obtain spectra over a larger area range and rapidly achieve large-scale coverage of hyperspectral remote sensing. However, the low signal-to-noise ratio of the interference signals limits the application of spectral data, especially when atmospheric detection occurs in the long-wavelength infrared (LWIR) band. In this paper, we construct an LWIR hyperspectral system of a Fourier transform spectrometer composed of a HgCdTe photovoltaic IR FPA and a Michelson interferometer. The LWIR interference signals are obtained by a high-frequency oversampling technique. We use the Kalman filter (KF) and its improved weighted adaptive Kalman filter (WAKF) to reduce the noise of multiple measured data of each pixel. The effect of overshoot and ringing artifacts on the objective signals is reduced by the WAKF. The applicability is studied by the interference signals from the different sampling frequencies and different pixels. The effectiveness is also verified by comparing the spectra of denoised interferograms with the reference spectrum. The experimental results show that the WAKF algorithm has excellent noise suppression, and the standard deviation of the interferogram can be reduced by 39.50% compared with that of KF. The WAKF is more advantageous in improving the signal-to-noise ratio of the interferogram and spectra. The results indicate that our system can be applied to atmospheric vertical detection and hyperspectral remote sensing over large area ranges because our denoised technique is suitable for large LWIR FPA.

An occupancy grid ruler based highly robust displacement measurement method and its application in brake wear assessment

The displacement of a multi-plate brake actuator provides insights into degradation. However, the displacement signal in a low sampling frequency is difficult to be assessed due to noise and outliers. This paper proposes an occupancy grid ruler (OGR) based displacement measurement method that sequentially implements displacement signal denoising and measurement value outlier removal and smoothing. The displacement and duration measurement values are extracted from the signal denoised by generalized Moreau envelope total variation. Next, the measurement values are assessed by the OGR, which has an updating function based on a binary Bayesian filter and inverse sensor model, and a time window with a specified observation length. Finally, the mass center of the grids with occupancy belief over the threshold is defined as the measurement result. A numerical simulation and experiments verified the proposed method is suitable for a quasi-static measurement with a given accuracy requirement, featuring dynamic performance and robustness.

Comparison on Quantitative Analysis of Olivine Using MarSCoDe Laser-Induced Breakdown Spectroscopy in a Simulated Martian Atmosphere

A Mars Surface Composition Detector (MarSCoDe) instrument mounted on Zhurong rover of Tianwen-1, adopts Laser-Induced Breakdown Spectroscopy (LIBS), with no sample preparation or dust and coatings ablation required, to conduct rapid multi-elemental analysis and characterization of minerals, rocks and soils on the surface of Mars. To test the capability of MarSCoDe LIBS measurement and quantitative analysis, some methods of multivariate analysis on olivine samples with gradient concentrations were inspected based on the spectra acquired in a Mars-simulated environment before the rover launch in 2020. Firstly, LIBS spectra need preprocessing, including background subtraction, random signal denoising, continuum baseline removal, spectral drift correction and wavelength calibration, radiation calibration, and multi-channel spectra subset merging. Then, the quantitative analysis with univariate linear regression (ULR) and multivariate linear regression (MLR) are performed on the characteristic lines, while principal component regression (PCR), partial least square regression (PLSR), ridge, least-absolute-shrinkage-and-selection-operator (LASSO) and elastic net, and nonlinear analysis with back-propagation (BP) are conducted on the entire spectral information. Finally, the performance on the quantitative olivine analyzed by MarSCoDe LIBS is compared with the mean spectrum and all spectra for each sample and evaluated by some statistical indicators. The results show that: (1) the calibration curve of ULR constructed by the characteristic line of magnesium and iron indicates the linear relationship between the spectral signal and the element concentration, and the limits of detection of forsterite and fayalite is 0.9943 and 2.0536 (c%) analyzed by mean spectra, and 2.3354 and 3.8883 (c%) analyzed by all spectra; (2) the R2 value on the calibration and validation of all the methods is close to 1, and the predicted concentration estimated by these calibration models is close to the true concentration; (3) the shrinkage or regularization technique of ridge, LASSO and elastic net perform better than the ULR and MLR, except for ridge overfitting on the testing sample; the best results can be obtained by the dimension reduction technique of PCR and PLSR, especially with PLSR; and BP is more applicable for the sample measured with larger spectral dataset.

Underwater acoustic signal denoising model based on secondary variational mode decomposition

Due to the complexity of marine environment, underwater acoustic signal will be affected by complex background noise during transmission. Underwater acoustic signal denoising is always a difficult problem in underwater acoustic signal processing. To obtain a better denoising effect, a new denoising method of underwater acoustic signal based on optimized variational mode decomposition by black widow optimization algorithm (BVMD), fluctuation-based dispersion entropy threshold improved by Otsu method (OFDE), cosine similarity stationary threshold (CSST), BVMD, fluctuation-based dispersion entropy (FDE), named BVMD-OFDE-CSST-BVMD-FDE, is proposed. In the first place, decompose the original signal into a series of intrinsic mode functions (IMFs) by BVMD. Afterwards, distinguish pure IMFs, mixed IMFs and noise IMFs by OFDE and CSST, and reconstruct pure IMFs and mixed IMFs to obtain primary denoised signal. In the end, decompose primary denoising signal into IMFs by BVMD again, use the FDE value to distinguish noise IMFs and pure IMFs, and reconstruct pure IMFs to obtain the final denoised signal. The proposed mothod has three advantages: (i) BVMD can adaptively select the decomposition layer and penalty factor of VMD. (ii) FDE and CS are used as double criteria to distinguish noise IMFs from useful IMFs, and Otsu algorithm and CSST algorithm can effectively avoid the error caused by manually selecting thresholds. (iii) Secondary decomposition can make up for the deficiency of primary decomposition and further remove a small amount of noise. The chaotic signal and real ship signal are denoised. The experiment result shows that the proposed method can effectively denoise. It improves the denoising effect after primary decomposition, and has good practical value.

Analysis of Savitzky-Golay Filter for Electrocardiogram De-Noising Using Daubechies Wavelets

Electrocardiogram (ECG) examination is of great importance in medical diagnosis of the cardiac disease, but wrong interpretation due to noise interference in the signal could be dangerous as this may lead to wrong diagnoses of patient’s heart condition. De-noising helps to reduce the noise level for a better interpretation of the signals. In this study, an analysis of Savitzky-Golay (S-G) filter for ECG de-noising using Daubechies wavelets has been carried out using MATLAB version 2015a. Noisy ECG signals downloaded from physionet.org under MIT-BIH arrhythmia database was de-noised using S-G filter of polynomial order 9 to data frames of length 21 displayed in both time and frequency domains while a quantitative evaluation was carried out to check the performance of the filter under signal-to-noise ratio (SNR), mean square error (MSE) and signal-to-interference ratio (SIR). Results show that de-noising using S-G filter for SNR, MSE, and SIR gives an average value of 32.78dB, 0.0001 and 1852.358dB respectively. This implies that the S-G filter helps eliminates the background noise as well as maintaining a good fit for our data, and also do not allow co-channel interference from other radio transmitters, which makes it an excellent filter for ECG signal de-noising. Hospitals management and cardiac health centers most understand the importance of these parameters in the selection of de-noising filters for good quality ECG in diagnosis and treatment of cardiac patients.

Deep learning-based system to predict cardiac arrhythmia using hybrid features of transform techniques

• Decomposed of ECG signals with discrete wavelet transform (DWT), empirical mode decomposition (EMD) and variational mode decomposition (VMD) methods. • Extraction of informative time-frequency based multi-domain features. • Ranking of features based on weightage in Chi-squared test and PSO search methods. • Classification of ECG beats using deep neural network. • Comparison of results with deep learning and discussion with published works. An early and accurate detection of arrhythmias is essential reduce the mortality rate due to cardiac diseases. Manual screening of the electrocardiogram (ECG) signals are time consuming, strenuous, and liable to human errors. This article proposes a deep learning approach for automated detection of cardiac arrhythmia using RCG signals fro MIT-BIH database. Various decomposition techniques namely: discrete wavelet transform (DWT), empirical mode decomposition (EMD) and variational mode decomposition (VMD) are used to de-noise the ECG signal. The time-frequency based multi-domain features are extracted from the various coefficients of the sub-bands from de-noised signals. These obtained features are ranked based on Chi-squared test and particle swarm optimization (PSO) based methods to select the best informative features for better classification accuracy. The hybrid features was classified with deep neural network (DNN) with ten-fold cross validation strategy in classifying five types of ECG beats. The best results was obtained with an accuracy of 99.75% with less computational complexity of 0.14 s using Chi squared selection approach. Thus the proposed model can be used in the hospitals set-up to automatically screen the abnormal ECG beats.