Noise Suppression Method Research Articles

Three closed loop noise suppression method for resonant micro optical gyroscope

For resonant micro-optical gyroscope (R-MOG), backscattering noise and optical Kerr noise are the main noises that affect the output accuracy of the gyroscope, and it is difficult to suppress them completely. This paper introduces a method to suppress the optical Kerr noise generated by the front end by compensating the optical power difference while suppressing the backscattered noise. Based on a single light source, the system changes the frequency difference between clockwise and counterclockwise optical paths through double-phase modulation, so as to improve the carrier rejection ratio and suppress the backscattered noise. At the same time, a high-speed feedback loop based on intensity modulator is designed, which is added to the optical loop to enhance the optical power, so as to reduce the impact of optical Kerr noise. The experimental results show that this method effectively suppresses the backscattered noise and optical Kerr noise, improves the output accuracy of the gyro by two orders of magnitude, and finally observes the long-term bias stability of 0.982°/h.

Improved Tucker decomposition algorithm for noise suppression of 3D GPR data in road detection

AbstractThe echo data of ground‐penetrating radar (GPR) have a low signal‐to‐noise ratio. Denoising and interference suppression are important for improving the accuracy of underground target recognition and detection. In this paper, a new method of noise analysis and suppression of 3D GPR is proposed, transforming the problem of noise reduction into an optimization problem regarding a third‐order tensor. This method is improved with the following features; a bidimensional empirical mode decomposition (BEMD) algorithm is employed to decompose the 3D GPR noise. The main source of the noise is determined by computing the standard deviation of each component decomposed. For the shortcoming of the existing GPR denoising methods that reduce the dimension of 3D GPR data, the proposed approach can denoise 3D GPR data directly to avoid the loss of data caused by reducing the dimension. This preserves the desired signal and extracts noise through the Tucker decomposition of 3D GPR data. An improved high‐order orthogonal iteration algorithm is utilized to optimize the decomposition. The peak signal‐to‐noise ratio (PSNR) of the signal for each survey line is calculated to evaluate the effectiveness of noise reduction. Simulations and real data sets are provided to compare the performance of algorithms. The results show that the mean PSNR after noise suppression with our method is 8.915 dB and 16.458 dB higher than wavelet transform and singular value decomposition algorithms, respectively. This demonstrates that the proposed approach can better suppress 3D GPR noise and provides technical support for improving 3D GPR data quality and road detection accuracy.

Seismic random noise suppression by using MSRD-GAN

Seismic exploration is a vital instrument for developing oil and gas reservoir resources, but the actual seismic data gathering process is usually plagued by noise interference. Therefore, reducing seismic data noise is critical for improving seismic data quality. This study proposed a seismic data noise suppression method based on a multi-scale residual density generative adversarial network (MSRD-GAN) to enhance seismic data quality. This network used local residual learning and density linking to establish multi-scale residual blocks (MSRBs) for multi-scale feature capture, avoiding the problem of the local perception of deep convolutional network features and the disappearance of hierarchically delivered features, which made it difficult to effectively recover signal details. It was also used with generative adversarial networks (GAN) to automatically learn the difference between noisy and legitimate seismic data signals, allowing it to suppress random noise while completely recovering valid signals. The MSRD-GAN was evaluated according to synthetic seismic data and field data to demonstrate its efficacy in suppressing seismic data noise. The experimental findings of both the synthetic and field data showed the benefits of the proposed MSRD-GAN in reducing complex random noise while preserving lower signal distortion.

Attentive Bi-LSTM-Based Method for Noise Suppression in Ambulatory ECG Measurements

Attentive Bi-LSTM-Based Method for Noise Suppression in Ambulatory ECG Measurements

A Novel Passive Millimeter Wave Image Noise Suppression Method Based on Pixel Non-local Self-similarity

A Novel Passive Millimeter Wave Image Noise Suppression Method Based on Pixel Non-local Self-similarity

Application of microphone arrays to interference cancellation by using null broadening beamforming

Beamforming is an important technology for array systems, the spatial filtering can be achieved by focusing the main beam to the target and null point to the interference. Comparing to the noise suppression method based single-channel, the signal from beamforming output shows lower distortion and higher signal-to-interference plus noise ratio. This study is to investigate the null broadening (bearing, width and depth) control of beamforming, the purpose is to overcome the bearing error between interference and null by producing a broadening null, and to improve the interference signal suppression. Considering the requirements of real-time and broadband in the acoustic system, the fixed beamforming process is chosen and combined with the convex optimization technique. There are several advantages form fixed process that are better robustness due to avoiding the signal self-cancellation from the issue of model mismatch and low-complexity computation contributes to achieving the real-time and broadband spatial filtering in the present process. In the experiment of the chirp signal, the SNR of beamformer output is increased from 2 to 10.5 dB, the correlation coefficient for target is increased from 0.52 to 0.82, and the interference is reduced from 0.54 to 0.07. In the experiment of the broadband voice signal, the SNR of beamformer output is increased from 2 to 6.5 dB, the correlation coefficient for target is increased from 0.37 to 0.68, and the interference is reduced from 0.58 to 0.19. The experimental results verify that the proposed algorithm can effectively controlled the capability of null broadening and have better interference suppression than other algorithms.

Feasibility of seismic wave communication using electromagnetic vibrator under strong environmental noise interference: Field experimental research.

Although various wired or wireless communication facilities play a significant role in underground mine production monitoring and disaster warning, some communication nodes will be damaged when an accident occurs, resulting in unreliable emergency communications. Due to the excellent transmission characteristics of seismic waves in the earth channel, there is no need for the seismic wave communication system to set up relay nodes. Since seismic waves are not easily disturbed by the external environment, they can be used as a new type of emergency communication carrier to realize information transmission. However, under strong background noise interference in a real environment, the feasibility of seismic wave communication using an electromagnetic vibrator is not yet known. Therefore, a field experiment research on electromagnetic vibrator seismic wave communication was conducted. First, the model of electromagnetic vibrator seismic wave communication was established. In addition, the on-off keying modulation method, the cross-correlation segment threshold demodulation method, and the noise suppression method based on the time-frequency characteristics of the carrier were introduced. Finally, an electromagnetic vibrator seismic wave communication system was built, and binary data communication up to 72m without deploying additional relay nodes was achieved successfully. The experimental results illustrate the feasibility of the electromagnetic vibrator seismic wave communication under strong background noise interference in a real environment. Furthermore, combined with the characteristics of the seismic wave, the time-frequency slice filtering can significantly improve the signal-to-noise ratio of the seismic wave and can further increase the communication distance with respect to the traditional methods.

Complex Magnetic Anomaly Detection Using Structured Low-Rank Approximation With Total Variation Regularization

In the field of magnetic anomaly detection (MAD), the anomaly signal is easily submerged by ambient electromagnetic interference. Though the existing noise suppression methods can effectively improve the signal-to-noise ratio (SNR), there are still some intractable problems, such as signal distortion and boundary blur. To solve these problems, a novel MAD method based on structured low rank (SLR) and total variation (TV) regularization constraints is proposed in this letter. The noise suppression performance is improved by leveraging the structured low rankness of the signal. To preserve clean boundaries of the anomalies, an anisotropic TV regularization constraint is employed in the approach. Comparing the SLR-TV method with four state-of-the-art methods with extensive field tests, the results demonstrate that the proposed SLR-TV method achieves the greatest SNR improvement by about 63.24% and the best structural similarity (SSIM) improvement by about 53.02% over other methods in the range from −40 to 0 dB, showing the utility and high fidelity of the proposed framework in low SNR.

Speech Separation Methodology for Hearing Aid

In the design of hearing aids (HA), the real-time speech-enhancement is done. The digital hearing aids should provide high signal-to-noise ratio, gain improvement and should eliminate feedback. In generic hearing aids the performance towards different frequencies varies and non uniform. Existing noise cancellation and speech separation methods drops the voice magnitude under the noise environment. The performance of the HA for frequency response is non uniform. Existing noise suppression methods reduce the required signal strength also. So, the performance of uniform sub band analysis is poor when hearing aid is concern. In this paper, a speech separation method using Non-negative Matrix Factorization (NMF) algorithm is proposed for wavelet decomposition. The Proposed non-uniform filter-bank was validated by parameters like band power, Signal-to-noise ratio (SNR), Mean Square Error (MSE), Signal to Noise and Distortion Ratio (SINAD), Spurious-free dynamic range (SFDR), error and time. The speech recordings before and after separation was evaluated for quality using objective speech quality measures International Telecommunication Union -Telecommunication standard ITU-T P.862.

Transient analysis of a noise suppression method with aerating techniques in capillary tubes

Flow-induced noise is closely related to the flow characteristics through an adiabatic capillary tube and a transition pipe, most existing methods for suppressing flow-induced noise are passive. An aerating technique is proposed based on the pressure feedback in the transition pipe to actively suppress flowinduced noise. The 3-D simulations of flashing are presented by performing the Schnerr-Sauer cavitation model coupling with the mixture model. For the turbulence model, the large eddy simulation approach is used. With the installation of aerating module, the pressure fluctuation in the transition pipe is weakened significantly, and the phenomenon of bubble collapse is suppressed. Numerical results illustrate that the transient pressure of the monitoring points downstream of the capillary outlet oscillates seriously due to the bubble bursting. The shedding process of the bubble group is observed according to the vaporliquid interface in the transition pipe. In addition, the oscillations of monitored transient pressure are suppressed with the application of aerating module. Then the noise source can be partially reduced actively in essence. This paper is devoted to understanding the two-phase flow characteristics of refrigerants in a transition pipe and presents a practical method to suppress noise near the capillary outlet.

Laser speckle noise suppression method based on empirical mode decomposition

Laser speckle noise suppression method based on empirical mode decomposition

A speckle noise suppression method based on surface waves investigation and monitoring data

A speckle noise suppression method based on surface waves investigation and monitoring data

Synergistic Hankel Structured Low-Rank Approximation With Total Variation Regularization for Complex Magnetic Anomaly Detection

In the field of magnetic anomaly detection (MAD), the anomaly signal is easy to be submerged by ambient electromagnetic interference. Though the existing noise suppression methods can effectively improve the signal-to-noise ratio (SNR), there are still some intractable problems, such as signal distortion and boundary blur. To solve these problems, a novel MAD method based on structured low-rank (SLR) and total variation (TV) regularization constraints is proposed in this paper. To be specific, a new framework SLR-TV, which mainly contains abnormal signal acquisition, objective function solution, and inverse transform operation is constructed. The noise suppression performance is improved by leveraging the structured low-rankness of the signal. To preserve clean boundaries of the anomalies, an anisotropic TV regularization constraint is employed in the approach. Comparing the SLR-TV with five state-of-the-art methods with extensive synthetic and field tests, the results demonstrate that the proposed SLR-TV method achieves the highest SNR improvement by about 15.62% and the best structural similarity (SSIM) improvement by about 62.95% over other methods in the range from -40 dB to 0 dB, showing the utility and high-fidelity of the proposed framework in low SNR.

Less Data-Dependent Seismic Noise Suppression Method Based on Transfer Learning With Attention Mechanism

Less Data-Dependent Seismic Noise Suppression Method Based on Transfer Learning With Attention Mechanism

Analysis of Seismic Signal and Detection of Abnormalities

Seismic signals are ground vibrations used to detect seismic events. However, seismic signal captured from sensors is distorted signal contains noise and makes actual event detection difficult. In most cases, external noise such as manmade or any heavy vehicle vibration always overlaps with the seismic reflections over time. The presence of noise in the seismic signal makes it difficult to determine the magnitude at which the seismic events have occurred. The aim of our study is to process the signals received from seismic sensor and identify it as seismic events signal and non-seismic events signal based on the magnitude. The authors propose a robust noise suppression method using bandpass filter, IIR Wiener filter and event detection using recursive Short-Term Average (STA)/Long Term Average (LTA) and Carl Short Term Average (STA)/Long Term Average (LTA). The proposed study determines reference magnitude to distinguish seismic and non-seismic activity. The projected study is based on the analysis of seismic signal received from single sensor and sensor networks (SN) and determines the magnitude to distinguish seismic and nonseismic events and time of an actual earthquake event. The experimental dataset is a broadband seismic signal from BSVK and CUKG station sensors located at Basavakalyan, Karnataka, and the Central University of Karnataka respectively. The proposed approach helps to extract the information about preseismic event, actual seismic event, post-seismic event activities and identify the abnormal pattern that supports to detect heearth’s activities before the actual seismic event.

Removal of strong noise in magnetotelluric data using grey wolf optimized wavelet threshold

AbstractTraditional magnetotelluric signal processing usually uses time–frequency transformation method. Wavelet is also a time–frequency transformation method that used to suppress the magnetotelluric noise. However, the selection of the threshold is very significant, and the unsuitable threshold will lead to excessive distortion of the reconstructed signal. Thus, we propose a method for magnetotelluric noise suppression using grey wolf optimized wavelet threshold. First, the magnetotelluric signal is decomposed by wavelet with appropriate wavelet basis and decomposition layers. The generalized cross‐validation criterion is used as the fitness function of grey wolf optimizer algorithm, which optimizes the threshold of each decomposition layer. Then, the detail coefficients of each layer and the maximum layer of the approximation coefficients are used in the optimized threshold. Next, the inverse wavelet transform is performed. Finally, the noise contour is obtained through iteratively searching for the optimal threshold, and the useful magnetotelluric signal is reconstructed. Simulation experiments and measured magnetotelluric data processing show that the large‐scale interference can effectively be suppressed, and the reconstructed magnetotelluric signal retains the more abundant low‐frequency of useful information. Compared with the remote reference method, fixed threshold method and Birge–Massart layered threshold method, the proposed method realizes the wavelet denoising with adaptive threshold selection in the magnetotelluric noise suppression. The results obtained show smoother and more continuous apparent resistivity–phase curves, which verifies the effectiveness of the optimization.

Comparative Study of the Performance of Seismic Waveform Denoising Methods Using Local and Near-Regional Data

ABSTRACTSeismic waveform data are generally contaminated by noise from various sources, which interfere with the signals of interest. In this study, we implemented and applied several noise suppression methods using data recorded by the regional network of the University of Utah Seismograph stations. The denoising methods, consisting of approaches based on nonlinear thresholding of continuous wavelet transforms (CWTs, e.g., Langston and Mousavi, 2019), convolutional neural network (CNN) denoising (Tibi et al., 2021), and frequency filtering, were all subjected to the same analyses and level of scrutiny. We found that for frequency filtering, the improvement in signal-to-noise ratio (SNR) decreases quickly with decreasing SNR of the input waveform, and that below an input SNR of about 32 dB the improvement is relatively marginal and nearly constant. In contrast, the SNR gains are low at high-input SNR and increase with decreasing input SNR to reach the top of the plateaus corresponding to gains of about 18 and 23 dB, respectively, for CWT and CNN denoising. The low gains at high-input SNRs for these methods can be explained by the fact that for an input waveform with already high SNR (low noise), only very little improvement can be achieved by denoising, if at all. Results involving 4780 constructed waveforms suggest that in terms of degree of fidelity for the denoised waveforms with respect to the ground truth seismograms, CNN denoising outperforms both CWT denoising and frequency filtering. Onset time picking analyses by an experienced expert analyst suggest that CNN denoising allows more picks to be made compared with frequency filtering or CWT denoising and is on par with the expert analyst’s processing that follows current operational procedure. The CWT techniques are more likely to introduce artifacts that made the waveforms unusable.

A Designed Calibration Approach for the Measurement-While-Drilling Instrument

The Measurement-While-Drilling (MWD) system, composed of a tri-axial magnetometer and a tri-axial accelerometer, is widely used in the Horizontal Directional Drilling machine in coal mines. This system can provide attitude information of each measuring point in the borehole, which will eventually allow the trajectory of the borehole to be drawn. The attitude information, however, showed a low-level accuracy, due to the sensor’s imperfection and mounting errors. The accuracy worsened when low-cost sensors were employed, as they had higher random noise. Therefore, an exploration of ways to eliminate the sensor imperfection and mounting tolerance as well as to suppress the noise is needed. In this paper, a feasible calibration approach was designed to address these issues. This new approach combined three foundational calibration algorithms, including the ellipsoidal fitting method, the planar fitting method, and the inner product invariance method. The traditional ellipsoidal fitting method and planar fitting method were optimized by using the recursive least square criterion and omitting the steps of sample data acquisition, respectively. In addition, the noise suppression method was involved in our approach to improve the calibration accuracy. The numerical simulation results showed that the number of sampling points decreased significantly, but the accuracy of the azimuthal angle and the pitch angle fully met the engineering requirements. The experimental results showed that the pitch angle error was reduced by less than 0.5°, and the azimuth error was also reduced by less than 2.5°. It should be noted that this new approach could be implemented without the help of other expensive auxiliary equipment.

Radiation Noise Reduction Using Noise Suppression Wires Installed on the Back of a 250-W-Class Photovoltaic Module

In recent years, the global movement toward decarbonization has accelerated, and renewable energy has attracted increasing attention. Among the various systems adopted for renewable power generation, photovoltaic (PV) power generation systems are becoming widespread as they have no moving or rotating parts and are easy to install. However, PV power generation modules generally emit noise during power generation and suffer from radio wave interference with the amplitude modulated radio band (531kHz to 1602kHz). In our previous study we confirmed radiation noise suppression with the installation of a noise suppression wire with a closed loop of copper wire in a 15-W-class PV module. In this study, we aim to clarify the noise suppression effect produced by a noise suppression wire installed on a 250-W-class PV module with a larger power generation capability than that in our previous study and a different internal structure of the PV module. Our results suggest that the proposed noise suppression method is effective for 250-W-class PV modules as well.

Noise suppression method for hydroxyl tagging velocimetry based on generative adversarial networks

Hydroxyl tagging velocimetry (HTV) technology is crucial in the velocimetry diagnosis of combustion flow fields. However, obtaining accurate HTV information in practical engineering applications is difficult because of complex flow fields and background noise interference. Therefore, for noise suppression, we proposed a generative adversarial network method for targeted network training based on the analysis of HTV image noise characteristics in a complex flow field and the construction of a high-confidence noise description model. The proposed method can effectively suppress noise in HTV experimental data, improve the signal-to-noise ratio of HTV images, and improve the accuracy of HTV measurement.