Joint Noise Reduction Research Articles

Practice and validation of inversion of seismic source localisation based on genetic algorithm

As the mining depth of coal resources increases, resulting in frequent mine earthquakes during mining. In this study, the rolling window ratio method is firstly chosen as the seismic phase recognition method to read the mine earthquake data received by the microseismic sensor. Secondly, the improved genetic algorithm is used as the optimization algorithm of the objective function to build the algorithmic framework of accurate inverse localization of mine earthquake. Finally, the accuracy of the algorithm for seismic source localization is verified based on actual engineering cases. Results show that the first arrival time extraction by the rolling window ratio method has the advantages of high accuracy and fast algorithm operation speed. The Fast Fourier Transform-Butterworth joint noise reduction method has a good noise reduction effect, which successfully suppresses the noise outside the mine earthquake signal and effectively improves the problem of much noise in the mine earthquake signal. Compared with the microseismic monitoring date, the mine earthquake localization error is within 5%.

Prediction of state-of-health of lithium-ion battery based on CEEMDAN-SG-LSTM combined model

State-of-health (SOH) is an important indicator for the maintenance and safe operation of batteries, and it is crucial for accurately predicting SOH. To address problems that the noise present in the original data lead to inaccurate prediction results. An Long-Short-Term-Memory (LSTM) method for SOH prediction is proposed based on the joint noise reduction model of complete ensemble empirical mode decomposition adaptive noise (CEEDMAN) and Savitzky-Golay (SG) filtering. Firstly, seven health indicators (HIs) were extracted by analyzing the voltage and current curves, and HIs with higher correlation with SOH were selected using Pearson correlation coefficient. Then, Intrinsic Mode Functions (IMF) components generated from SOH by CEEMDAN are divided into noise-component, noise-dominant-component, useful-signal-dominant-component, filtered noise-dominant-component and useful-signal-dominant-component are reconstructed into filtered SOH. Finally, the LSTM model is used for SOH prediction. Experiments show that proposed model captures the capacity regeneration phenomenon well with high prediction accuracy, and errors are all below 1.9%.

A new fault diagnosis of rolling bearing on FFT image coding and L-CNN

To address the problems of low diagnostic accuracy and slow diagnostic speed of the convolutional neural network (CNN) fault diagnosis method in rolling bearing diagnosis, a new rolling bearing fault diagnosis method based on fast Fourier transform (FFT) image coding and lightweight-CNN (L-CNN) is proposed. The method is mainly divided into three stages: firstly, the original signal is reconstructed by noise reduction using a joint noise reduction method of complete ensemble empirical mode decomposition with adaptive noise, permutation entropy, and wavelet threshold denoise; then, the frequency spectra and phase spectra feature fusion data of the noise-reduced and reconstructed bearing vibration signals are obtained by FFT, the feature fusion data are encoded into a heat map, and the image coding data-set is fed into an improved L-CNN for fault diagnosis. Experiments were carried out using the Guangdong University of Petrochemical Technology bearing fault data-set and the Case Western Reserve University bearing fault data-set with diagnostic accuracies of 98.75% and 99%, respectively. The results demonstrate that the method can effectively classify bearing fault vibration signals with the advantages of a fast diagnosis, high accuracy, and good generalization ability.

Leakage identification for mineral air supply pipeline system based on joint noise reduction and ELM

This work presents a leakage identification framework for the coal mine air supply pipeline system based on joint noise reduction and extreme learning machine (ELM). First, leakage vibration signals from a pipeline system are sampled. Then, vibration signals are decomposed by parameter-optimized variational mode decomposition (POVMD), where the mode number is determined via the energy difference principle, and the separation point of low- and high-frequency modes are identified by mutual information entropy. Next, the wavelet denoising method based on an improved threshold function is implemented on high-frequency modes. Then denoised vibration signals are reconstructed by denoised high-frequency modes and low-frequency modes. Subsequently, multi-scale entropy values are extracted and fed into ELM for training and testing. In the laboratory environment, the ELM model with 132 hidden nodes and the Sigmoid function shows the highest testing accuracy (94.17%), indicating that the proposed method is more practical and effective than other methods.

Research on SVM-Based Bearing Fault Diagnosis Modeling and Multiple Swarm Genetic Algorithm Parameter Identification Method

The bearing fault diagnosis of petrochemical rotating machinery faces the problems of large data volume, weak fault feature signal strength and susceptibility to noise interference. To solve these problems, current research presents a combined ICEEMDAN-wavelet threshold joint noise reduction, mutual dimensionless metrics and MPGA-SVM approach for rotating machinery bearing fault diagnosis. Firstly, we propose an improved joint noise-reduction method of an Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and wavelet thresholding. Moreover, the noise-reduced data are processed by mutual dimensionless processing to construct a mutual dimensionless index sensitive to bearing fault features and complete the fault feature extraction of the bearing signals. Furthermore, we design experiments on faulty bearings of multistage centrifugal fans in petrochemical rotating machinery and processed the input data set according to ICEEMDAN-wavelet threshold joint noise reduction and mutual dimensionless indexes for later validation of the model and algorithm. Finally, a support vector machine model used to effectively identify the bearing failures, and a multi-population genetic algorithm, is studied to optimize the relevant parameters of the support vector machine. The powerful global parallel search capability of the multigroup genetic algorithm is used to search for the penalty factor c and kernel parameter r that affect the classification performance of the support vector machine. The global optimal solutions of c and r are found in a short time to construct a multigroup genetic algorithm-support vector machine bearing fault diagnosis and identification model. The proposed model is verified to have 95.3% accuracy for the bearing fault diagnosis, and the training time is 11.1608 s, while the traditional GA-SVM has only 89.875% accuracy and the training time is 17.4612 s. Meanwhile, to exclude the influence of experimental data on the specificity of our method, the experimental validation of the Western Reserve University bearing failure open-source dataset was added, and the results showed that the accuracy could reach 97.1% with a training time of 14.2735 s, thus proving that the method proposed in our paper can achieve good results in practical applications.

Speech Denoising and Compensation for Hearing Aids Using an FTCRN-Based Metric GAN

Hearing aids aims to improve speech intelligibility for hearing impaired patients to levels comparable to those for normal hearing listeners. However, the interference of environmental noises greatly increase the difficulty of hearing loss compensation. Most related research only focuses on one aspect of noise reduction and hearing loss compensation. In this letter, we propose a metric generative adversarial framework based on a frequency-time convolution recurrent network for joint noise reduction and hearing loss compensation. The audiogram is extended along the frequency axis to form embedded features. A metric discriminator is introduced and the optimization of the generator is guided by an evaluation score related to hearing loss compensation. Additional perceptual-based losses are set to stabilize optimization. Experimental results show that the proposed method can better reduce noise and compensate for hearing loss compared with other algorithms.

Rolling bearing fault diagnosis based on improved VMD-adaptive wavelet threshold joint noise reduction

Due to the fault vibration signal of the rolling bearing is greatly interfered by the background noise, the fault features are easily submerged and result in a low fault diagnosis accuracy. A novel fault diagnosis method of rolling bearing is proposed based on improved VMD-adaptive wavelet threshold combined with noise reduction in this paper. Firstly, the modal components are obtained based on VMD decomposition; Secondly, the dual determination criteria of sample entropy and correlation coefficient are constructed to filter the components; Subsequently, an adaptive wavelet thresholding function is proposed, and quadratic noise reduction is applied to mixed IMFs, which in turn reconstructs each component to achieve joint noise reduction. Finally, based on traditional machine learning and deep learning diagnosis methods, the features of noise reduction signals are extracted to realize fault diagnosis. By verifying and analyzing the simulated signal with the measured signal, noise components, the expression of fault characteristics, and the accuracy of fault diagnosis are eliminated, enhanced, and improved.

Adaptive combined noise reduction for helicopter tail transmission system

Weak vibration signal processing has always been a research hotspot. This is critical for both signal extraction and troubleshooting. Aiming at the weakness of the early fault of the helicopter tail transmission system and the aliasing of the vibration signal, this paper performs joint noise reduction filtering on the vibration signal and extracts the early failure signal from the strong noise scene. A method of extracting early failure signal based on ICA is proposed, combined with VMD+SVD noise reduction to extract weak vibration signals of transmission system. The results show that the MSE and SNR indicators after using Adaptive Combined Noise Reduction are better than the general noise reduction method, and the initial signal is also retained while filtering the noise.

Application of Joint Filter Based on EMD and IIR in SINS

Abstract To solve the problem that the interference of vibration noise in the strapdown inertial navigation system (SINS) affects navigation accuracy, a joint noise reduction method based on Empirical Mode Decomposition (EMD) decomposition and Infifinite Impulse Response (IIR) low-pass digital filter was proposed. On the basis of EMD decomposition, Finally, SINS vehicle test is carried out. The results show that the combined noise reduction method based on joint noise reduction method based on EMD decomposition and IIR low-pass digital filter has an obvious effect on the noise reduction of accelerometer signal. After filtering, the horizontal positioning error of INS is better than 0.29 nautical miles, and the navigation accuracy is improved by 31%. Therefore, the joint noise reduction method based on EMD decomposition and IIR low-pass digital filter proposed in this paper can effectively improve the navigation accuracy of SINS.

Joint multipath signals and noise reduction in passive radar

The paper considers the multipath signal and noise reduction problem in passive radar. A unified framework is provided for the joint reduction of multipath signals and noise in both Gaussian and non-Gaussian circumstances. The traditional clutter cancellation methods are incorporated in this framework corresponding to the Gaussian circumstance. Based on this framework, a joint reduction method (JRM) is developed for suppressing the multipath signals and noise. Specifically, the cancellation of noise is explicitly discussed. Analytical analysis of the proposed method is included. An approximate implementation of the JRM (approximate implementation of the joint reduction method) which increases the computational efficiency is also provided. Simulations demonstrate the effectiveness of the proposed methods.

Improved spatialization performance for joint speech dereverberation and noise reduction in binaural hearing aids

ObjectiveIn this work we propose a unified solution for joint speech dereverberation, noise reduction and spatial preservation of either diffuse noise fields or single-point noise sources in binaural hearing aid applications, which is based on the multichannel Wiener filter (MWF) with preservation of the interaural coherence (MWF-IC) method. MethodsThe original interaural coherence (IC) auxiliary penalty cost function in the MWF-IC, which comprises information about both interfering noise and late reverberation component, is modified to consider the interference noise information only. It provides a new regularization form for preserving both interaural time difference (ITD) and IC binaural cues of the residual noise. This approach is theoretically supported by recent findings about the relationship between IC and ITD binaural cues for point acoustic sources. ResultsExtensive simulation results with objective measures, as well as psychoacoustic experiments with normal hearing volunteers indicate that the proposed method outperforms the preservation of spatial localization cues of its original version, keeping approximately the same performance in terms of noise-reduction and speech-dereverberation. ConclusionThe proposed scheme expands the MWF-IC original spatial cue preservation capability from only diffuse noise fields to either diffuse noise fields or a single-point noise source. SignificanceThis is the first MWF-based speech dereverberation and noise reduction method for binaural hearing aid that provides perceptually relevant preservation of acoustic scenarios comprised of one speech source and either a single-point noise source or a diffuse noise field.

Fault diagnosis of rolling bearings based on impulse feature enhancement and time-frequency joint noise reduction

Aiming at the problem that the effectiveness of impulse feature enhancement (IFE) depends on the duration of high-level (or low-level) K and the number of high-level L, we regard a segmented impulse norm as the fitness function and combine it with the whale optimization algorithm to select the optimal parameters adaptively. Time-frequency joint noise reduction (TFJNR) is also proposed to suppress the noise components in the signal. Simulation and experimental results of rolling bearings indicate that the proposed algorithm can rapidly select the optimal parameters K and L to ensure the performance of IFE, while TFJNR has the ability to suppress the noise components in the signal. Fast kurtogram, empirical mode decomposition, and fast spectral correlation are also used for comparison. The results highlight the performance of the proposed algorithm.

Quantization-Aware Binaural MWF Based Noise Reduction Incorporating External Wireless Devices

For hearing-impaired listeners, both ambient noise suppression and binaural cues preservation of directional sources are required, such that a complete spatial awareness of the acoustic scene can be obtained. It was shown that the binaural multichannel Wiener filter (MWF) with partial noise preservation can achieve joint noise reduction and binaural cues preservation and incorporating an external microphone signal improves the performance of binaural MWFs. Motivated by this, we propose a binaural MWF incorporating external wireless devices in this paper. First, we theoretically analyze the performance of the MWF in terms of output signal-to-noise ratio (SNR) and binaural cues preservation errors. As in practice the external devices are power driven with a limited amount of battery resource and the power consumption heavily depends on the transmission rate, given an expected noise reduction performance we then optimize the bit-rate for a single external microphone. Further, we consider to minimize the total power consumption over multiple external devices under a constraint on the output SNR, which turns out to be a rate distribution problem. The proposed rate-distributed binaural MWF is evaluated using a hearing-aid setup with various dynamics. It is shown that the proposed method can obtain a desired SNR at a much lower bit-rate, and an expected trade-off between SNR gain and binaural cues preservation accuracy can be obtained by optimizing the bit-rates. Increasing the bit-rates improves both instrumental speech quality and speech intelligibility.

Joint Contrast Enhancement and Noise Reduction of Low Light Images Via JND Transform

Low light images suffer from a low dynamic range and severe noise due to low signal-to-noise ratio (SNR). In this paper, we propose joint contrast enhancement and noise reduction of low light images via just-noticeable-difference (JND) transform. We adopt the JND transform to achieve both contrast enhancement and noise reduction based on human visual perception. First, we generate a JND map based on an the human visual system (HVS) response model from foreground and background luminance, called JND transform. Second, for base image, we perform perceptual contrast enhancement based on luminance adaptation to effectively allocate a dynamic range to each gray level while preventing under enhancement (tone distortion) and over-enhancement. Third, we refine the JND map using Weber's law, luminance adaptation and visual masking. Weber's law enhances the JND map based on the luminance variation after contrast enhancement. Luminance adaptation suppresses noise for smooth regions, while visual masking enforces detail enhancement for textural regions. Fourth, we perform inverse JND transform to generate the enhanced luma channel from the JND map and base image. Finally, we conduct chroma denoising by transferring texture information of the enhanced luma channel to the chroma channels with guided filtering. Experimental results show that the proposed method achieves both contrast enhancement and noise reduction for low light images as well as outperforms state-of-the-art methods in terms of quantitative measurements.

An integrated multi-channel approach for joint noise reduction and dereverberation

The performance of speech communication and automatic speech recognition systems greatly degrades in real conditions where reverberation and noise exist. In this paper, we propose an integrated multi-channel speech enhancement approach for joint noise reduction and dereverberation based on the multi-channel Wiener filter (MWF) and the weighted prediction error (WPE). Specifically, the input signal of MWF is derived from WPE that is considered as a preprocessor. MWF is implemented as a minimum variance distortionless response (MVDR) beamformer followed by a single-channel Wiener filter. The covariance matrix of the undesired signal in MWF is approximated using the prediction in WPE. In the implementation of MVDR beamformer, the relative early transfer functions (RETFs) are used rather than either the entire relative transfer functions (RTFs) or only the direct-path of desired speech signal, which are estimated by covariance whitening method. The target signal variance in WPE is further updated with the output signal. Experimental results show that this proposed approach outperforms the state-of-art approaches in reducing late reverberation and noise, with the reduction of word error rate of 38.50% in 3 m recorded data conditions.

A new joint noise reduction and echo suppression system based on FBSS and automatic voice activity detector

In hands-free communication framework, the main drawback affecting the system is the presence of the acoustic noise and echo interferences. To address these problems at the same time, we propose a new joint noise reduction and echo suppression system based on forward blind source separation (FBSS) structure and automatic voice activity detection (AVAD). To deal with the acoustic echo issue, we propose to use an echo suppressor (ES) based on the single channel normalized least mean square (SCNLMS) algorithm. In order to obtain an accurate estimate of the echo signal, the punctual noise is pre-processed by a noise reduction (NR) process, where, for the NR process, we propose to use the two channel normalized least mean square (TCNLMS) algorithm based on the FBSS structure. The FBSS structure uses often manual voice activity detection (MVAD) mechanism to separate speech from noise, but this is not practice in real situations. To overcome the use of the MVAD, we propose a system that uses a new automatic voice activity detection (automatic VAD) mechanism based on signal-to-noise-ratio (SNR) estimation. The obtained results in terms of objective criteria have shown the efficiency of the proposed system in dealing with both interferences at the same time.

Dimensionality Reduction Based on PARAFAC Model

Abstract In hyperspectral image analysis, dimensionality reduction is a preprocessing step for hyperspectral image (HSI) classification. Principal component analysis (PCA) reduces the spectral dimension and does not utilize the spatial information of an HSI. To solve it, the tensor decompositions have been successfully applied to joint noise reduction in spatial and spectral dimensions of hyperspectral images, such as parallel factor analysis (PARAFAC). However, the PARAFAC method does not reduce the dimension in the spectral dimension. To improve it, two new methods were proposed in this article, that is, combine PCA and PARAFAC to reduce both the dimension in the spectral dimension and the noise in the spatial and spectral dimensions. The experimental results indicate that the new methods improve the classification compared with the PARAFAC method.

Stimulus-aware spatial filtering for single-trial neural response and temporal response function estimation in high-density EEG with applications in auditory research

A common problem in neural recordings is the low signal-to-noise ratio (SNR), particularly when using non-invasive techniques like magneto- or electroencephalography (M/EEG). To address this problem, experimental designs often include repeated trials, which are then averaged to improve the SNR or to infer statistics that can be used in the design of a denoising spatial filter. However, collecting enough repeated trials is often impractical and even impossible in some paradigms, while analyses on existing data sets may be hampered when these do not contain such repeated trials. Therefore, we present a data-driven method that takes advantage of the knowledge of the presented stimulus, to achieve a joint noise reduction and dimensionality reduction without the need for repeated trials. The method first estimates the stimulus-driven neural response using the given stimulus, which is then used to find a set of spatial filters that maximize the SNR based on a generalized eigenvalue decomposition. As the method is fully data-driven, the dimensionality reduction enables researchers to perform their analyses without having to rely on their knowledge of brain regions of interest, which increases accuracy and reduces the human factor in the results. In the context of neural tracking of a speech stimulus using EEG, our method resulted in more accurate short-term temporal response function (TRF) estimates, higher correlations between predicted and actual neural responses, and higher attention decoding accuracies compared to existing TRF-based decoding methods. We also provide an extensive discussion on the central role played by the generalized eigenvalue decomposition in various denoising methods in the literature, and address the conceptual similarities and differences with our proposed method.

Joint Noise Reduction for Contrast Enhancement in Stokes Polarimetric Imaging

Contrast optimization is a key issue in polarimetric imaging for the purpose of target detection. In practice, the noise could induce the intensity fluctuation of the image and thus lead to the decrease of the image contrast. A joint noise reduction method is proposed for contrast enhancement in Stokes polarimetric imaging. The proposed method is based on the relation of the joint polarimetric image set, which includes four images taken to calculate Stokes vector and one image taken at the optimal state of a polarization state analyzer (PSA). By our method, the traditional contrast-enhanced image is modified to decrease the disturbance of noise, and the contrast of the image is further enhanced. Both the theoretical analysis and the real-world experimental results demonstrate that our method can effectively decrease the disturbance by the noise and thus increase the contrast of the image. In particular, it is found that the effect of the proposed method is independent of the optimal PSA state when the regular tetrahedron measurement matrix is implemented.

Dimensionality reduction based on parallel factor analysis model and independent component analysis method

In hyperspectral image (HSI) analysis, dimensionality reduction is a preprocessing step for HSI classification. Independent component analysis (ICA) reduces the spectral dimension and does not utilize the spatial information of the HSI. To solve it, tensor decompositions have been successfully applied to joint noise reduction in spatial and spectral dimensions of HSIs, such as parallel factor analysis (PARAFAC). However, the PARAFAC method does not reduce the dimension in the spectral dimension. We proposed a method to improve it, which combines ICA and PARAFAC to reduce both the dimension in the spectral dimension and the noise in the spatial and spectral dimensions. The experimental results indicate that this method improves the classification compared with the previous methods.