Impulsive Noise Research Articles

Field comparison of Antarctic krill (Euphausia superba) backscatter and aggregation types using NORTEK and SIMRAD echosounders

Abstract Temporal distributions of Antarctic krill (Euphausia superba) density and aggregation types were characterized and compared using Nortek Signature100 and SIMRAD Wideband Autonomous Transceiver (WBAT) upward-looking echosounders. Noise varied between the two echosounders. With the Signature100, it was necessary to correct data for background, transient, and impulse noises, while the WBAT data needed to be corrected for background noise only. For selected regions with no visible backscatter, the signal-to-noise ratio of Sv values (i.e. the ratio between the signal and the background noise level) did not vary between the two echosounders. Surface echo backscatter was similar during similar time periods. Descriptive metrics were used to quantify spatial and temporal krill vertical distributions: volume backscatter, mean depth, center of mass, inertia, equivalent area, aggregation index, and proportion occupied. Krill backscatter density differed between the two instruments but was detected at similar mean depths. Krill aggregations were identified at each mooring location and classified in three types based on morphological characteristics. Each type of aggregation shape differed at the two spatially separated moorings, while the acoustic density of each aggregation type was similar. The Signature100 detected a lower number of krill aggregations (n = 133) compared to the WBAT (n = 707). Although both instruments can be used for autonomous deployment and sampling of krill over extended periods, there is a strong caveat for the use of the Signature100 due to significant differences in noise characteristics and krill detection.

Adaptive cyclic content ratiogram: a new signal decomposition method for bearing concurrent fault diagnosis

Fast kurtogram (FK) has been proven to be an effective tool for resonance frequency band detection, which is widely used in bearing fault diagnosis. However, FK is not robust to impulsive noise, and its frequency band segmentation rule is fixed, which leads to over-decomposition or under-decomposition of the fault resonance frequency band in its signal decomposition results. Therefore, an adaptive cyclic content ratiogram is proposed in this paper. Firstly, based on the energy distribution of vibration signals on different frequency components, the frequency spectral segmentation is performed adaptively, and multiple sub-signals containing different frequency components are obtained. Secondly, the ratio of cyclic content (RCC), which cannot only more accurately characterize the cyclostationarity of bearing fault impacts but also be insensitive to impulsive noise, is applied to evaluate the fault feature information contained in each sub-signal separately. In the meanwhile, considering that fault characteristic frequency information is required in the process of RCC evaluation, the proposed method performs adaptive fault characteristic frequency detection for each sub-signal based on the envelope spectrum. The RCC maximization is used to locate the fault resonance frequency band. Also, combined with the estimated fault characteristic frequencies, the proposed method can achieve the extraction of concurrent fault features. Simulation and experimental data verify the effectiveness of the proposed method.

Projection FxLMS framework of active noise control against impulsive noise environments

This paper focuses on active noise control in the presence of disturbance from an α-stable distribution. The filtered-x fractional lower-order covariance algorithm has been introduced to enhance the performance of traditional algorithms in impulsive noise environments. For minimal adjustment of the weight vector, another filtered-x normalized fractional lower-order covariance (FxNFLOC) algorithm is proposed. Furthermore, the projection approach of the filtered-x least mean square (FxLMS) algorithm is explored. Within this framework, a new algorithm called filtered-x least mean logarithmic square (FxLMLS), which employs a logarithmic function, is devised. The convergence conditions of the developed schemes are also derived. Numerical examples demonstrate the effectiveness and superior performance of the FxNFLOC and FxLMLS algorithms in handling impulsive noise over existing competitors in various scenarios.

M-estimate based diffusion active noise control algorithm over distributed networks and its performance analysis

Multi-channel active noise control (ANC) systems can achieve effective attenuation of low-frequency noise in spatial areas and have been extensively studied. Recent works have focused on the application of multitask diffusion strategies in ANC systems based on wireless acoustic sensor and actuator networks (WASAN), which show the advantages of saving computational burden and enhancing flexibility. However, these works all adopt the mean-square error minimization as the optimization criterion, which leads to severe performance degradation in the presence of impulsive noise interference. Therefore, in this paper, we exploit the M-estimate function and the augmented weight vector of the control filter to formulate a robust minimization problem and propose a distributed augmented diffusion filtered-x least mean M-estimate (DADFxLMM) algorithm for multi-channel ANC systems. The proposed algorithm leverages the M-estimate function to overcome the influence of outliers in the error signal and achieves robustness against impulsive noise, and also eliminates the dependence on the symmetry of the acoustic paths by integrating the augmented weight vectors. Moreover, we replace the score function with the update probability of the weight vector to avoid the need for integration and Price’s theorem, and analyze the mean and mean-square performance of the proposed DADFxLMM algorithm under the contaminated Gaussian (CG) noise model. Simulation results under different system configurations demonstrate that the proposed algorithm outperforms the existing multi-channel ANC algorithms in the presence of impulsive noise interference.

The pickleball seven decibel challenge

Noise emission from outdoor pickleball play and its potential impact to surrounding community receptors is often characterized as an impulsive source of sound, which would be consistent with ISO 1996-1 definition for a regular impulsive sound source that includes "outdoor ball games" as an example. But some recent noise assessments including those that may be found online are leaning towards elevating pickleball noise emission to the highly impulsive category and thus affording it a 12 dB adjustment instead of the 5 dB adjustment for regular impulsive noise--hence, a 7 dB difference that if confirmed would make pickleball noise compliance with applicable community regulations and policies much more difficult. This paper elaborates on the impulse noise category distinction as it may apply to pickleball play, and summarizes apparent support (or lack thereof) to cross the seven-decibel divide and how that might influence court development growth for the rapidly growing pickleball sport and potentially affected neighboring communities.

A comparison of impulsive noise metrics

Impulsive sounds are transient acoustic events that can draw undesirable attention because of their abrupt nature, for example sounds produced by weights dropping onto floors in buildings. A metric that represents human experience of these sounds can guide efforts to mitigate them. This paper investigates metrics developed for impulsive sounds generated by weight or ball drops and by IT equipment. The metric for IT equipment is an impulsiveness metric formulated with psychoacoustic bands and loudness descriptions. The metric for weight or ball drops is the fast time-weighted sound pressure level implemented with various filtering schemes. The ability of the metrics in predicting subjective human experience of IT equipment transients is compared. Metric values for sounds generated by ball drops onto various floor assemblies on a concrete slab are compared.

Application of Machine Learning Algorithms to Analyze the Clinical Characteristics of NIHL Caused by Impulse Noise and Steady Noise.

Occupational hearing loss of workers exposed to impulse noise and workers exposed to steady noise for a long time may have different clinical characteristics. As of May 2019, all 92 servicemen working in a weapon experimental field exposed to impulse noise for over 1 year were collected as the impulse noise group. As of Dec 2019, all 78 servicemen working in an engine working experimental field exposed to steady noise for over 1 year were collected as the steady noise group. The propensity score matching (PSM) model was used to eliminate the imbalance of age and working time between the two groups of subjects. After propensity score matching, 51 subjects in each group were finally included in the study. The machine learning model is constructed according to pure tone auditory threshold, and the performance of the machine learning model is evaluated by accuracy, sensitivity, specificity, and AUC. Subjects in the impulse noise group and the steady noise group had significant hearing loss at high frequencies. The hearing of the steady noise group was worse than that of the impulse noise group at speech frequency especially at the frequency of 1 kHz. Among machine learning models, XGBoost has the best prediction and classification performance. The pure tone auditory threshold of subjects in both groups decreased and at high frequency. The hearing of the steady noise group at 1 kHz was significantly worse than that of the impulse noise group. XGBoost is the best model to predict the classification of our two groups. Our research can guide the prevention of damage caused by different types of noises.

Least total logistic distance metric algorithm and its variable step-size version

Classical mean-square error (MSE)-based adaptive filtering algorithms are useful for noise-free inputs. However, if the input signal of the adaptive filter with such an adaptive filtering algorithm is corrupted by noise, it will suffer from serious degradation of steady-state performance. To address the problem, a new adaptive filtering algorithm is proposed in this work. This algorithm not only uses the total method to compensate the bias caused by noisy input but also minimizes the cost function based on logistic distance metric (LDM) to achieve robustness against impulsive noise. Compared with the existing algorithms, the proposed least total logistic distance metric (LTLDM) algorithm has less misalignment when the input signal is disturbed by noise and has good robustness to impulsive noise. This work also tackles the crucial trade-off between convergence rate and misalignment by developing a variable step-size for LTLDM. In addition, to give deep insight into the stochastic behavior of the proposed LTLDM, its mean-square deviation is analyzed at steady state. The advantage of LTLDM and the accuracy of theoretical expressions are verified by extensive simulations.

An intelligent impulsive noise mitigation with deep learning method

AbstractTo enable message transmission among sensors and equipment, power line communication (PLC) is a widely adopted smart grid. However, due to the occurrence of impulsive noise (IN), reliable transmissions over PLC channels in the smart grid are challenging. Therefore, in this paper, we propose an adaptive noise mitigation scheme to clip the IN with the sliding window‐based method, where the altitude of the received signal in the current time slots is obtained by computing the average altitude of signals in the previous and next time slots. To detect the states of IN and dynamically estimate the power threshold of signals for the IN mitigation scheme, we develop an intelligent algorithm based on the long short‐term memory network. To prevent the useful signals from being eliminated as IN signals, we propose the accelerated proximal gradient method (APGM) based on tone reservation to reduce the peak‐to‐average power ratio (PAPR) for the transmitting signals with low computational complexity. In addition, the closed‐form expression of the bit error rate (BER) is derived for the proposed sliding window‐based IN mitigation scheme according to the probability density function of the IN. Simulation results demonstrate that the proposed IN mitigation scheme achieves a better BER performance than the conventional IN mitigation schemes. In addition, the APGM aided by IN mitigation can further improve BER performance due to the PAPR reduction.

Artificial immune systems for data augmentation

We study object detection models and observe that their respective architectures are vulnerable to image distortions such as noise, compression, blur, or snow. We propose alleviating this problem by training the models with antibodies generated using Artificial Immune Systems (AIS) from original training samples (antigens). These antibodies are AIS-distorted antigens at the pixel level through cycles of “select, clone, mutate, select” until an affinity to the antigen is achieved. We then add the antibodies to the antigens, train the models, validate and test them under 15 distortions, and show that our data augmentation approach (AISbod) significantly improved their accuracy without altering their architecture or inference speed. For example, the DINO object detector under the COCO dataset improves by 4% under clean samples, by 6.50% on average over all 15 distortions, by 2.15% under snow, and by 27.60% under impulse noise. Our simulations show that our method performs better under distortions and clean samples than related defense methods and is more consistent across datasets and object detection models. For instance, our method is, on average, 70% better than the closest related method across 15 distortions for the evaluated models under COCO. Moreover, we show that our approach to image classification and object tracking models significantly improves accuracy under distortions. We provide the code of our method and the DINO model trained using our method at https://github.com/moforio/AISbod.

A novel feedforward narrow-broadband hybrid active noise control system for excavator interior noise control

Excavator interior noise is a mixture of engine noise and impulsive noise produced by working device. Hybrid narrow-broadband active noise control (HANC) systems can efficiently eliminate the traditional mixed broad-narrowband noise. However, these HANC systems have drawbacks such as high computational complexity, unable to cope with impulsive noise and inability to obtain the best performance due to parameter selection method. To solve these problems, a novel feedforward narrow broadband HANC system consisting of four subsystems, namely, sinusoidal noise canceller (SNC) subsystem, robust broadband active noise control (RBANC) subsystem, delay narrowband active noise control (DNANC) subsystem, and supporting error calculating subsystem, is proposed. Combining the advantages of delayed least mean square algorithm in DNANC subsystem and Versoria criterion-based filtered-x least mean square algorithm in RBANC subsystem, both the computational efficiency and the robustness when encountering impulsive noise of the proposed HANC system are efficiently improved. In addition, the performance of the proposed HANC system is further improved by changing the key parameters selection method form trial-and-error method to improved particle swarm optimization (IPSO) algorithm with active factor. Both the superiority of the proposed HANC system over existing counterparts and the advantage of the IPSO algorithm with active factor in determining the key parameters are demonstrated by extensive simulations. Real-time HANC experiments in semi-anechoic chamber based on measured excavator interior mixed noise are conducted in this article. Experimental results illustrate that the proposed HANC system achieves good noise elimination performance and also being robust when encountering impulsive noise.

Robust Tensor Completion via Capped Frobenius Norm.

Tensor completion (TC) refers to restoring the missing entries in a given tensor by making use of the low-rank structure. Most existing algorithms have excellent performance in Gaussian noise or impulsive noise scenarios. Generally speaking, the Frobenius-norm-based methods achieve excellent performance in additive Gaussian noise, while their recovery severely degrades in impulsive noise. Although the algorithms using the lp -norm ( ) or its variants can attain high restoration accuracy in the presence of gross errors, they are inferior to the Frobenius-norm-based methods when the noise is Gaussian-distributed. Therefore, an approach that is able to perform well in both Gaussian noise and impulsive noise is desired. In this work, we use a capped Frobenius norm to restrain outliers, which corresponds to a form of the truncated least-squares loss function. The upper bound of our capped Frobenius norm is automatically updated using normalized median absolute deviation during iterations. Therefore, it achieves better performance than the lp -norm with outlier-contaminated observations and attains comparable accuracy to the Frobenius norm without tuning parameter in Gaussian noise. We then adopt the half-quadratic theory to convert the nonconvex problem into a tractable multivariable problem, that is, convex optimization with respect to (w.r.t.) each individual variable. To address the resultant task, we exploit the proximal block coordinate descent (PBCD) method and then establish the convergence of the suggested algorithm. Specifically, the objective function value is guaranteed to be convergent while the variable sequence has a subsequence converging to a critical point. Experimental results based on real-world images and videos exhibit the superiority of the devised approach over several state-of-the-art algorithms in terms of recovery performance. MATLAB code is available at https://github.com/Li-X-P/Code-of-Robust-Tensor-Completion.

Hider: A Hyperspectral Image Denoising Transformer With Spatial-Spectral Constraints for Hybrid Noise Removal.

Hyperspectral image (HSI) qualities are limited by a mixture of Gaussian noise, impulse noise, stripes, and deadlines during the sensor imaging process, resulting in weak application performance. To enhance HSI qualities, methods based on convolutional neural networks have been successively applied to restore clean data from the observed data. However, the architecture of these methods lacks spectral and spatial constraints, and the convolution operators have limited receptive fields and inflexible model inferences. Thus, in this study, we propose an efficient end-to-end transformer, named HSI denoising transformer (Hider), for mixed HSI noise removal. First, a U-shaped 3-D transformer architecture is built for multiscale feature aggregation. Second, a multihead global spectral attention module within the spectral transformer block is designed to excavate information in different spectral patterns. Finally, an additional locally enhanced cross-spatial attention module within the spatial-spectral transformer block is constructed to build the long-range spatial relationship to avoid the high computational complexity of global spatial self-attention. Through the imposition of global correlations along spectrum and spatial self-similarity constraints on the transformer, our proposed Hider aims to capture long-range spatial contextual information and cluster objects with the same spectral pattern for HSI denoising. To verify the effectiveness and efficiency of Hider, we conducted extensive simulated and real experiments. The denoising results on both simulated and real-world datasets show that Hider achieves superior evaluation metrics and visual assessments compared with other state-of-the-art methods.

Machine-learning based detection of marine mammal vocalizations in snapping-shrimp dominated ambient noise

Passive acoustics is an effective method for monitoring marine mammals, facilitating both detection and population estimation. In warm tropical waters, this technique encounters challenges due to the high persistent level of ambient impulsive noise originating from the snapping shrimp present throughout this region. This study presents the development and application of a neural-network based detector for marine-mammal vocalizations in long term acoustic data recorded by us at ten locations in Singapore waters. The detector’s performance is observed to be impeded by the high shrimp noise activity. To counteract this, we investigate several techniques to improve detection capabilities in shrimp noise including the use of simple nonlinear denoisers and a machine-learning based denoiser to suppress the noise level. These are shown to enhance the detection performance significantly. Using the robust detectors developed, we discuss some of the vocalizations detected over three years of our acoustic recorder deployments.

A Descent Matrix-free Nonlinear Conjugate Gradient Algorithm for Impulse Noise Removal

The convergence of the Polak, Ribie‘re-Polyak (PRP) conjugate gradient (CG) method requires some modifications for improved theoretical properties. In this article, we explore an optimal choice of the Perry conjugacy condition to propose a hybrid CG parameter for solving optimization and inverse problems, particularly in an image reconstruction model. This parameter is selected to satisfy a combination of revised version of the PRP and Dai-Yuan (DY) CG methods. The numerical implementation includes inexact line search, showcasing the scheme's robustness (highest number of solved functions) compared to other known CG algorithms. The efficiency is shown in terms of Real error (RelErr), peak signal noise ratio (PNSR), and CPU time in seconds for impulse noise removal while for unconstrained minimization problems, the study evaluated the efficiency based on number of iterations, function evaluation, and CPU time in seconds. An interesting feature of the proposed method is its ability to converges to the minimizer regardless of the initial guess, relying on certain established assumptions.

Exploring the Efficacy of Nonlinear Filters in CMOS for 2-D Signal Processing for Image Quality Enhancement.

This study rigorously investigates the effectiveness of nonlinear filters in CMOS for 2-D signal processing to enhance image quality. We comprehensively compare traditional linear filters' performance, which operate on the principle of linearity, with nonlinear filters, such as the median-median (Med-Med) approach, designed to handle nonlinear data. To ensure the validity of our findings, we use widely accepted metrics like normalized squared error (NSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) to quantify the differences. Our simulations and experiments, conducted under controlled conditions, demonstrate that nonlinear filters in CMOS outperform linear filters in removing impulse noise and enhancing images. We also address the challenges of implementing these algorithms at the hardware level, focusing on power consumption and chip area optimization. Additionally, we propose a new architecture for the Med-Med filter and validate its functionality through experiments using a 9-pixel image sensor array. Our findings highlight the potential of nonlinear filters in CMOS for real-time image quality enhancement and their applicability in various real-world imaging applications. This research contributes to visual technology by combining theoretical insights with practical implementations, paving the way for more efficient and adaptable imaging systems.

A Novel DOA Estimation Algorithm Based on Robust Mixed Fractional Lower-Order Correntropy in Impulsive Noise

The estimation of direction of arrival (DOA) is paramount in the realm of practical array signal processing systems. Nevertheless, traditional estimation methods often rely heavily on the Gaussian noise assumption, rendering them ineffective in achieving high-precision estimates in environments plagued by strong impulsive noise. To address this challenge, this paper introduces a novel DOA estimation algorithm that leverages mixed fractional lower-order correntropy (MFLOCR) in the context of Alpha-stable distributed impulsive noise. Correntropy is used as a measure of the similarity of the signals, using a Gaussian function to smooth extreme values and provide greater robustness against impulsive noise. By utilizing diverse kernel lengths to jointly regulate the kernel function, the concept of correntropy is expanded and implemented in the fractional lower-order moment (FLOM) algorithm for received signals. Subsequently, the MFLOCR is derived by adjusting the resulting form of correntropy. Finally, an enhanced DOA estimation algorithm is proposed that combines the MFLOCR operator with the MUSIC algorithm, specifically tailored for impulsive noise environments. Furthermore, a proof of boundedness is provided to validate the effectiveness of the proposed approach in such noisy conditions. Simulation experiments confirmed that the proposed method outperforms existing DOA estimation methods in the context of intense impulsive noise, a low generalized signal-to-noise ratio (GSNR), and a smaller number of snapshots.

Objective sound quality evaluation of the electric two-stage centrifugal compressor for fuel cell vehicles-experimental investigations

Sound quality is one of the most challenging studies in the field of automotive NVH. The most important noise source in fuel cell vehicles (FCVs) is the air compressor, and its noise seriously affects the sound quality of FCVs. The centrifugal compressor is now the mainstream choice for commercial FCVs, but there are few studies on its sound quality evaluation using objective psychoacoustic parameters. Therefore, a comprehensive vibration-noise experiment is conducted on an electric two-stage centrifugal compressor (ETCC) for FCVs to evaluate the sound quality accurately, including all operating conditions. The sound quality is evaluated by four objective psychoacoustic parameters: loudness, sharpness, fluctuation strength and roughness, which are the most widely used indicators. The reasons for the variation of these acoustic parameters under dynamic operating conditions are accordingly explained. It can be concluded that the loudness and sharpness show a strong and positive correlation with the rotational speed, while the fluctuation strength and roughness just reflect the sound quality of the ETCC for the low-frequency band and high-frequency band, respectively. So, taking loudness and sharpness as the core indicators under accelerating conditions is recommended. Then, the sound quality of the ETCC under unstable conditions at variable speeds is investigated. It is found that the sound quality of the ETCC deteriorates under surge conditions. When entering into the deep surge, the loudness and sharpness both fluctuate drastically, which is caused by the periodic impulse noise. The fluctuation strength rises sharply and keeps a high level in the process of deep surge because of the obvious low-frequency characteristics of the surge. The above three parameters have a clear correlation with the rotational speed, while roughness is the only parameter that is not significantly connected with the rotational speed, which can be explained by the existence of a high-frequency fluctuation under the entire surge process. Finally, the causes of sound quality deterioration are explored using secondary signal processing. The results show that the outlet pressure pulsations of the ETCC under surge conditions lead to the increases and fluctuations of these acoustic parameters, and compressor surge is the main cause of deterioration in sound quality for FCVs. Based on the results, the objective sound quality evaluation of the ETCC is accomplished, and four psychoacoustic parameters can be modified to improve the sound quality. This study provides a theoretical basis for proposing methods to accurately evaluate and improve the sound quality of FCVs.

A novel Laplacian vector directional filter designed for real-time removal of impulsive noise from color images

Eliminating noise from a color image represents an essential task in image processing, given that noise can markedly diminish visual quality and impact the precision of subsequent analyses or applications (such as visual quality inspection, medical image analysis, etc.). In this study, we introduce an innovative approach designed to remove noise while preserving the edges and fine details of a color image. This method is based on Laplacian operators, threshold values for identifying potentially corrupted pixels due to noise, and the Vector Directional Filter (VDF) for substituting noisy pixels. Consequently, this technique is termed Laplacian VDF (LVDF). Additionally, a Hardware/Software (HW/SW) design is formulated for the real-time implementation of the LVDF filter. The hardware architecture is designed through the High-Level Synthesis (HLS) flow, while the software component is executed using the ARM Cortex-A53 hardcore processor. Furthermore, five Direct Memory Access (DMAs) are employed to enhance data throughput between the hardware coprocessor and Double Data Rate (DDR) memory. Performance evaluations conducted on the ZCU102 kit reveal that the LVDF HW/SW design facilitates the restoration of 74 images per second, resulting in a 94 % reduction in execution time compared to the software implementation, all while maintaining consistent objective and subjective image quality.

Enspectrumix: Novel adaptive methodology for fault component extraction from vibration mixtures

Machine condition monitoring is important in industry to monitor machine health conditions and avoid unexpected machine breakdowns. Vibration mixtures contain sufficient machine health information, but they have lots of vibration interferences. Therefore, it is crucial to adaptively extract fault components from vibration mixtures. Blind-filter-based methods have been extensively studied to extract fault components and they use impulsiveness and cyclo-stationarity properties as prior knowledge to optimize signal processing parameters. The difficulties of state-of-the-art methods include that: (1) statistical indices (e.g., kurtosis, Gini index, correlated kurtosis, etc.) cannot simultaneously characterize impulsiveness and cyclo-stationarity properties so that blind-filter-based methods are always affected by impulsive noise and low-frequency components, resulting in unreliable performance for fault components extraction; (2) optimization of signal processing parameters is time-consuming; (3) band-in noise cannot be removed; (4) only a frequency band rather than several frequency bands can be adaptively selected, resulting in the incomplete fault components extraction. Instead of using the aforementioned prior knowledge, this paper reports a new perspective that use healthy vibration mixtures as prior knowledge to improve the existing blind-filter-based methods and provide more reliable fault components extraction for practical implementation in industry. Firstly, an ensemble difference spectrum-based new index is proposed to provide a completely new perspective for traditionally existing statistical indices. Subsequently, it is theoretically proved that the new index satisfies two new properties for quickly and better quantifying fault components. Next, a new methodology named Enspectrumix is designed to decompose a raw vibration mixture into sub-signals and use the newly designed index to adaptively select informative sub-signals existing in different frequency bands. The effectiveness and superiorities of the proposed Enspectrumix are validated by two real-world dataset cases. Moreover, the Enspectrumix can be extended to extract concerned difference components in many other domains.