Noise Suppression Research Articles

Noise suppression for an aeromagnetic measurement system on an unmanned helicopter.

An unmanned helicopter is one of the main platforms for conducting unmanned aerial vehicle aeromagnetic measurements and combines the advantages of rotary-wing and fixed-wing unmanned aerial vehicles. However, unmanned helicopter-based aeromagnetic measurement systems face complex static magnetic noise and maneuvering magnetic interference, which limit their practical performance. To address this issue, an improved multi-channel frequency measurement algorithm for the optically pumped magnetic sensor is proposed to suppress the static magnetic noise proportional to the frequency noise generated by the random quantization error and the airborne electromagnetic interference. A novel aeromagnetic compensation method for the maneuvering magnetic interference is then proposed to weaken the negative effects of the strong multicollinearity of the attitude parameters of the unmanned helicopter on the compensation accuracy and stability by introducing a regularization term and weight matrix. In addition, dedicated software is developed for the real-time calculation and compensation of magnetic interference fields. A dedicated unmanned-helicopter-based aeromagnetic measurement system is developed, and ground and flight experiments are carried out. The ground test results indicate that the static noise of the proposed system is only 0.000 82 nT. In the flight experiments, the system achieves an improvement ratio of 8.33, which is higher than the improvement ratio of 4.37 for a state-of-the-art commercial compensator. Furthermore, the dynamic noise after compensation decreases by 37.6% from 0.0157 to 0.0098 nT.

An optimal sensor layout method based on noise reduction estimation for active road noise control

Active noise control (ANC) is an effective method to suppress vehicle road noise. The rapid selection of the reference sensor set and rational selection of its layout position are crucial for determining the vibration noise transmission paths with high contributions, which determines the noise reduction performance of the active road noise control (ARNC) system. The reference sensor set selected by the conventional multiple coherence function (MCOH) method does not always ensure the optimal noise reduction performance and the matrix singularity problem based on the Fisher information matrix (FIM) expansion method. To alleviate this problem, this paper proposes a method based on the overall level of noise reduction (OANR) and the FIM to select a set of reference sensors and determine their positions. This method uses an optimal causally constrained Wiener filter to estimate the theoretical OANR for each control area to ensure the reference sensor selected has the optimal noise reduction performance, and it expands efficiently based on the FIM method to select a set of reference sensors. Besides, the matrix singularity problems in the FIM-based expansion method are solved using noise frequency summation and matrix operation, which makes the method more universally applicable. Numerical simulations are performed to analyze and evaluate the noise reduction performance and selection efficiency of the proposed method under different conditions. In addition, a series of real vehicle ANC experiments are conducted. The results show that the reference sensor set selected based on the proposed method has an obvious advantage in noise reduction performance, and only four reference sensors are needed to achieve 4.63 dB(A) noise reduction in ARNC experiments. It fully confirms the effectiveness of the proposed method in real vehicle applications.

Low-rank prior-based Fast-RPCA for clutter filtering and noise suppression in non-contrast ultrasound microvascular imaging

Accurate and real-time separation of blood signal from clutter and noise signals is a critical step in clinical non-contrast ultrasound microvascular imaging. Despite the widespread adoption of singular value decomposition (SVD) and robust principal component analysis (RPCA) for clutter filtering and noise suppression, the SVD’s sensitivity to threshold selection, along with the RPCA’s limitations in undersampling conditions and heavy computational burden often result in suboptimal performance in complex clinical applications. To address those challenges, this study presents a novel low-rank prior-based fast RPCA (LP-fRPCA) approach to enhance the adaptability and robustness of clutter filtering and noise suppression with reduced computational cost. A low-rank prior constraint is integrated into the non-convex RPCA model to achieve a robust and efficient approximation of clutter subspace, while an accelerated alternating projection iterative algorithm is developed to improve convergence speed and computational efficiency. The performance of the LP-fRPCA method was evaluated against SVD with a tissue/blood threshold (SVD1), SVD with both tissue/blood and blood/noise thresholds (SVD2), and the classical RPCA based on the alternating direction method of multipliers algorithm through phantom and in vivo non-contrast experiments on rabbit kidneys. In the slow flow phantom experiment of 0.2 mm/s, LP-fRPCA achieved an average increase in contrast ratio (CR) of 10.68 dB, 9.37 dB, and 8.66 dB compared to SVD1, SVD2, and RPCA, respectively. In the in vivo rabbit kidney experiment, the power Doppler results demonstrate that the LP-fRPCA method achieved a superior balance in the trade-off between insufficient clutter filtering and excessive suppression of blood flow. Additionally, LP-fRPCA significantly reduced the runtime of RPCA by up to 94-fold. Consequently, the LP-fRPCA method promises to be a potential tool for clinical non-contrast ultrasound microvascular imaging.

A study on multi-channel active sound profiling algorithm for hybrid control of broadband and narrowband noise inside vehicles

The vehicle interior noise consists of both broadband road noise and narrowband engine order noise. Currently, the road noise control (RNC) system and engine noise control (EOC) system operate independently in engineering applications. This paper introduces a novel multi-channel hybrid broadband and narrowband active noise control (HBNANC) algorithm, designed to simultaneously attenuate road noise and engine order noise inside vehicles. On this basis, by integrating an error signal design (ESD) subsystem and a sinusoidal error separation (SES) subsystem, a multi-channel hybrid broadband and narrowband active sound profiling (HBNASP) algorithm is further proposed, enabling specific attenuation or amplification of individual narrowband components to meet subjective acoustic comfort requirements. The paper validates the algorithms’ effectiveness and robustness through simulations, and real vehicle experimental measurements under various conditions. The research offers a comprehensive approach to enhancing the auditory experience inside vehicles, contributing to the advancement of active noise control technology.

AGD-GAN: Adaptive Gradient-Guided and Depth-supervised generative adversarial networks for ancient mural sketch extraction

To address the overlooked issues of multi-scale detail feature extraction and disease noise suppression in mural sketch extraction, we proposed a novel generative adversarial network with Adaptive Gradient-guided and Depth-supervised (AGD-GAN), which can generate high-quality mural sketches in an unsupervised manner. AGD-GAN first enriches mural feature details at various scales by introducing a cross-channel residual attention module, significantly improving the detail extraction effects. The Adaptive Gradient-guided strategy is based on the gradient attention maps, which can adaptively adjust the weights between gradient information and detail feature preservation according to the degree of damage in the mural, further balancing the preservation of mural detail features and the suppression of disease noise. Finally, the Depth-supervised reinforces constraints on the position and shape of the sketches by introducing a depth-predicted loss function, thereby reducing background noise interference and controlling the shape of the generated sketches. We compared with eight state-of-the-art algorithms quantitatively and qualitatively, experimental results demonstrate the promising capability of the proposed AGD-GAN to extract clear, coherent, and comprehensive mural sketches. We release the source code at https://github.com/Alice77bai/AGD-GAN.

MA_W-Net-Based Dual-Output Method for Microseismic Localization in Strong Noise Environments

With the continuous depletion of conventional oil and gas reservoir resources, the beginning of exploration and development of unconventional oil and gas reservoir resources has led to the rapid development of microseismic monitoring technology. Addressing the challenges of low signal-to-noise ratio and inaccurate localization in microseismic data, we propose a new neural network MA_W-Net based on the U-Net network with the following improvements: (1) The foundational U-Net model was refined by evolving the single-channel decoder into a two-channel decoder, aimed at enhancing microseismic event localization and noise suppression capabilities. (2) The integration of attention mechanisms such as the convolutional block attention module (CBAM), coordinate attention (CA), and squeeze-and-excitation (SE) into the encoder to bolster feature extraction. We use synthetic data for evaluating the proposed method. Comparing with the normal U-net network, our accuracy in seismic recordings with a signal-to-noise ratio of −15 is improved from 78 percent to 93.5 percent, and the average error is improved from 2.60 m to 0.76 m. The results show that our method can accurately localize microseismic events and denoising processes from microseismic records with a low signal-to-noise ratio.

O2SAT: Object-Oriented-Segmentation-Guided Spatial-Attention Network for 3D Object Detection in Autonomous Vehicles

Autonomous vehicles (AVs) strive to adapt to the specific characteristics of sustainable urban environments. Accurate 3D object detection with LiDAR is paramount for autonomous driving. However, existing research predominantly relies on the 3D object-based assumption, which overlooks the complexity of real-world road environments. Consequently, current methods experience performance degradation when targeting only local features and overlooking the intersection of objects and road features, especially in uneven road conditions. This study proposes a 3D Object-Oriented-Segmentation Spatial-Attention (O2SAT) approach to distinguish object points from road points and enhance the keypoint feature learning by a channel-wise spatial attention mechanism. O2SAT consists of three modules: Object-Oriented Segmentation (OOS), Spatial-Attention Feature Reweighting (SFR), and Road-Aware 3D Detection Head (R3D). OOS distinguishes object and road points and performs object-aware downsampling to augment data by learning to identify the hidden connection between landscape and object; SFR performs weight augmentation to learn crucial neighboring relationships and dynamically adjust feature weights through spatial attention mechanisms, which enhances the long-range interactions and contextual feature discrimination for noise suppression, improving overall detection performance; and R3D utilizes refined object segmentation and optimized feature representations. Our system forecasts prediction confidence into existing point-backbones. Our method’s effectiveness and robustness across diverse datasets (KITTI) has been demonstrated through vast experiments. The proposed modules seamlessly integrate into existing point-based frameworks, following a plug-and-play approach.

Automatic dysarthria detection and severity level assessment using CWT-layered CNN model

Dysarthria is a speech disorder that affects the ability to communicate due to articulation difficulties. This research proposes a novel method for automatic dysarthria detection (ADD) and automatic dysarthria severity level assessment (ADSLA) by using a variable continuous wavelet transform (CWT) layered convolutional neural network (CNN) model. To determine their efficiency, the proposed model is assessed using two distinct corpora, TORGO and UA-Speech, comprising both dysarthria patients and healthy subject speech signals. The research study explores the effectiveness of CWT-layered CNN models that employ different wavelets such as Amor, Morse, and Bump. The study aims to analyze the models’ performance without the need for feature extraction, which could provide deeper insights into the effectiveness of the models in processing complex data. Also, raw waveform modeling preserves the original signal’s integrity and nuance, making it ideal for applications like speech recognition, signal processing, and image processing. Extensive analysis and experimentation have revealed that the Amor wavelet surpasses the Morse and Bump wavelets in accurately representing signal characteristics. The Amor wavelet outperforms the others in terms of signal reconstruction fidelity, noise suppression capabilities, and feature extraction accuracy. The proposed CWT-layered CNN model emphasizes the importance of selecting the appropriate wavelet for signal-processing tasks. The Amor wavelet is a reliable and precise choice for applications. The UA-Speech dataset is crucial for more accurate dysarthria classification. Advanced deep learning techniques can simplify early intervention measures and expedite the diagnosis process.

A study on active road noise control based on operational transfer path analysis and selective subband adaptive filtering

In the feedforward active road noise control (ARNC) system, the noise reduction performance relies on the correlation between the reference signals and the target noise signals. To achieve better noise reduction performance, the operational transfer path analysis (OTPA) model with singular value decomposition was proposed to separate the road noise component from the target noise. Combined with OTPA and multi-coherence sorting method (MCSM), simulation analysis and real vehicle tests under various speeds both showed that the system with optimized reference sensor set had better road noise reduction performance, especially at the peak frequencies. To further improve noise reduction performance and reduce computational load, a selective subband adaptive filtering (SSAF) algorithm was proposed to build an active road noise control model with optimized reference sensor set. Simulation results, employing real vehicle test data, showed that the proposed method further improved the noise reduction performance and effectively reduced the amount of computational load.

Design and simulation of a bioimpedance detection analog front-end targeting medical applications

This paper presents the design of a bio-impedance detection analog front-end system, which is critical for continuous monitoring of physiological signals in the prevention and treatment of diseases such as coronary heart disease in the context of an aging population. The analog front-end system employs a capacitive-coupled chopper instrumentation amplifier with a fully differentially folded cascode operational amplifier as the core amplifier, and a common-mode feedback loop is introduced to improve the common-mode rejection ratio due to the high requirement for noise suppression. The power supply voltage of the design is 3.3V, achieving a total current consumption of 45uA and a total power consumption of 0.15mW. The core operational amplifier provides a maximum open-loop gain of 58 dB and a -3dB bandwidth of 8.2KHz. The power supply rejection ratio for the positive supply and ground achieved values of 102dB and 108dB, respectively. The common-mode rejection ratio of the chopper instrumentation amplifier can reach 109 dB, which is critical for suppressing common-mode noise.

Coherent Noise (Ground roll) Consideration: Local Wave-field Decomposition (LWD) Based Approaches to Suppression

There is always a signal and a noise component in seismic data. Noise can be defined as any recorded energy that tampers with the desired signal of interest. Although it might be difficult to separate signal from noise due to the variety of noise kinds, effective noise suppression is crucial for high-quality imaging. Surface waves or ground roll are a prominent source-generated coherent noise in exploration seismology. Ground roll contains little to no information about the deeper subsurface as it moves laterally through the earth’s near surface. The low-frequency contents and strong amplitudes of these arrivals often mask recordings of body waves reflected by deeper subsurface targets. This discussion paper will describe ground rolls, a significant form of seismic coherent noise, as well as their unique characteristics, how they appear on acquired seismic data, the significance of suppressing them in seismic processing applications, and an examination of local wave-field decomposition (LWD) techniques and their applications.

Optimized equalization based on MAP detection for C-band bandwidth-limited IM/DD systems.

Entangled dynamic and deterministic inter-symbol interferences (ISIs) induced by complicated channel impairments, limit the transmission capacity of intensity modulation and direct detection (IM/DD) systems. This Letter proposes a colored noise-suppressed channel shortening filter (CNS-CSF)-enabled maximum a posteriori (MAP) estimation (CNS-CSF-MAP) scheme to disentangle and mitigate deterministic and dynamic ISIs, where the CNS-CSF is deployed to perform the optimized dynamic ISI equalization with equalization-enhanced noise suppression, and the subsequent MAP algorithm is used to eliminate the residual deterministic ISI. The performance of the CNS-CSF-MAP scheme is evaluated and demonstrated in a C-band 61-Gb/s 100-km optical on-off keying (OOK) IM/DD system. The experimental results show that the proposed CNS-CSF-MAP scheme reaches the 20% and 7% forward error correction (FEC) thresholds at received optical powers (ROPs) of -6.6 dBm and -4 dBm, achieving 0.5- and 1.5-dB gains over a conventional post-filter-enabled MAP (PF-MAP) scheme.

THEORETICAL ANALYSIS OF THE APPLICATION OF PSEUDO-NOISE SIGNALS IN MARINE PULSE ELECTROMAGNETIC PROSPECTING

Using numerical modeling of a useful signal based on a characteristic geoelectric model of underwater permafrost and real recordings of a noise signal, we conducted a comparative analysis of noise suppression during transient sounding using a marine towed dipole-dipole array in accumulation modes with opposite-polar current pulses and pseudo-noise signals (PNS). For a series of current sequences in the form of PNS with different durations and numbers of pulses, as well as for a signal in the accumulation mode, with the superposition of an identical noise signal, transient sounding curves were obtained corresponding to the geoelectric model under consideration at a recording time of the order of 8-13 s per station. Based on the results of comparison of the obtained curves reconstructed from noisy synthetic data in the accumulation and PNS modes, it was established that in the PNS mode with pulses of duration 100 μs and 1 ms, the relative error in the PNS mode on average over the profile turns out to be significantly lower (up to 1.5 times) than a similar one error in the accumulation mode, and remains within an acceptable value (up to several percent) until later times.

Experimental study of active noise control for a full-scale plenum window in a domestic apartment

Plenum windows, also known as ventilation windows, allow for natural ventilation in living spaces while providing noise reduction to improve the health and living comfort of occupants, especially in noise-polluted and tropical/sub-tropical cities. However, while plenum windows can provide acceptable levels of natural ventilation, the passive noise performance of plenum windows when fully opened is still lacking as compared to the conventional closed windows- especially in the low frequencies experimentally. Active noise control (ANC) has shown good performance in several applications for low-frequency control. Thus, the application of ANC using a single-channel and decentralized dual-channel ANC implementation, informed by prior simulation studies is developed and field tested for a plenum window in a domestic setting built to reproduce the environment of a high-rise apartment. The experiments show that the average global attenuation benefit in the apartment room due to a single-channel ANC is 2.9 dB in the 100 Hz – 500 Hz range while the decentralized dual-channel ANC provides up to an average of 4.8 dB reduction in the 100 Hz – 500 Hz range or an average reduction of 3.8 dB in the 100 Hz – 800 Hz range. The decentralized dual-channel ANC is also tested under different noise types and is effective in low-frequency control. Furthermore, the dual-channel ANC remains robust even with an additional mechanical ventilation system operating inside the plenum window, improving the natural ventilation.

Passive laser power stabilization in a broadband noise spectrum via a second-harmonic generator.

An extremely conspicuous passive power noise stabilization is the first, to the best of our knowledge, discovered in a cavity-enhanced second-harmonic generation (SHG) process. Differing from the SHG as a buffer reservoir, the stronger strength of the nonlinear interaction pushes the power noise suppression level to a higher value and exhibits a broadband noise reduction performance due to the mechanism of dynamic pump suppression in the SHG process. The noise is suppressed to near shot noise limit (SNL) among the kHz to MHz frequency range, accompanied by a maximum noise reduction of 35 dB. A comprehensive demonstration indicates that the nonlinear interaction has no function on the phase noise of fundamental and harmonic waves. A theoretical model is also established that is consistent well with the experimental results. The methodology is beneficial to multiple optical metrology experiments.

HDetect-VS: Tiny Human Object Enhancement and Detection Based on Visual Saliency for Maritime Search and Rescue

Strong sun glint noise is an inevitable obstruction for tiny human object detection in maritime search and rescue (SAR) tasks, which can significantly deteriorate the performance of local contrast method (LCM)-based algorithms and cause high false alarm rates. For SAR tasks in noisy environments, it is more important to find tiny objects than localize them. Hence, considering background clutter and strong glint noise, in this study, a noise suppression methodology for maritime scenarios (HDetect-VS) is established to achieve tiny human object enhancement and detection based on visual saliency. To this end, the pixel intensity value distributions, color characteristics, and spatial distributions are thoroughly analyzed to separate objects from background and glint noise. Using unmanned aerial vehicles (UAVs), visible images with rich details, rather than infrared images, are applied to detect tiny objects in noisy environments. In this study, a grayscale model mapped from the HSV model (HSV-gray) is used to suppress glint noise based on color characteristic analysis, and large-scale Gaussian Convolution is utilized to obtain the pixel intensity surface and suppress background noise based on pixel intensity value distributions. Moreover, based on a thorough analysis of the spatial distribution of objects and noise, two-step clustering is employed to separate objects from noise in a salient point map. Experiments are conducted on the SeaDronesSee dataset; the results illustrate that HDetect-VS has more robust and effective performance in tiny object detection in noisy environments than other pixel-level algorithms. In particular, the performance of existing deep learning-based object detection algorithms can be significantly improved by taking the results of HDetect-VS as input.

Speckle Noise Detection and Removing by Machine Learning Algorithms in Multisensory Images for 5G Transmission

The multispectral satellite sensor images have multibands, which have some typical noise. There is difficult to detect this tipical noise with low resolution image. The satellite local or gloval pixel information and quantificationare degraded due to this noise. Many standard transformations and filtering operations are developed for detection and removing of non-gaussion noise, which are not given sophisticated results with existing methods. These statistical characteristics are applied to those samples to identify and quantify present tipical noise. The higher-order statistical based machine learning algorithm is developing to remove the speckle noise from satellite image. In this proposed algorithm, implemented the higher order statistical approache such as skewness, kurtosis based adaptive curvelet methods are implemented for the detection and suppression of speckle noise with retrieve of spectral and spectral values. The proposed algorithm preserves smooth and sharp details and maintains the tradeoff level in multispectral bands is suitable for advanced high speed 5G communication with the effective rate of transmission. The proposed results are verified with suitable statistical parameters such as PSNR, Entropy and ERGAS values.

Enhancing active noise control through stacked autoencoders: Training strategies, comparative analysis, and evaluation with practical setup

This work is motivated by the imperative to overcome the limitations of conventional active noise control (ANC) techniques. These limitations are rooted in linear systems like the least mean square algorithm when faced with nonlinear distortions in the acoustic environment and system electronics, such as loudspeakers. This work addresses the nonlinear ANC problem by conceptualizing ANC as a deep learning problem. A novel stacked autoencoder (SAE) model is proposed, which is trained to estimate the noise that should be played at a loudspeaker so that the noise received at an error microphone may be attenuated. ANC experiments are initially set up in an anechoic room to study the fundamental performance characteristics of the architecture without extraneous considerations of room impulse response. This is followed by performance analysis in a normal reflective room. Actual noise, comprising broadband and tonal noise, is recorded in both an anechoic room and a normal reflective room. The importance of these experiments in the design process lies in their ability to account for nonlinear effects in actual setups, surpassing the limitations of relying solely on models. Experiment outcomes demonstrate that the proposed SAE model yields a noise reduction (NR) of up to 26.44 dB for wideband noise and up to 35.74 dB for tonal noise types. Additionally, there is an improvement in the extent of the quiet zone provided by the proposed SAE for tonal noise cases as compared to the traditional filtered-x least mean square (FxLMS) technique.

Response Behavior of Inverse Diffusion Flame to Low Frequency Acoustic Field

ABSTRACT Conducting the investigation of flame dynamics perturbed by acoustic waves had guiding significance for flame extinguishing technology. In this paper, the behavior of inverse diffusion flame under the influence of 200–300 Hz sound waves was reported for the first time. Detailed analysis of the entire process of flame from steady state to extinction, including flame structure, periodic characteristics, temperature and KL factor distribution. The relationship between flame morphology parameters and acoustic parameters was established through dimensionless analysis. In addition, the flame extinguishing performance of sound waves in this frequency band was measured and analyzed. The results indicated that the flame could still maintain the typical inverse diffusion flame shape under low sound pressure, and the pulsation frequency was stable. However, when the sound pressure rose to a certain level, the flame began to pulsation in a disorderly manner. In addition, with the increase of sound pressure, the flame morphology would undergo significant changes, and the temperature and KL factor showed a trend of first increasing and then decreasing. The mechanism of sound wave suppression of flames was related to the unsteady flow generated by sound waves. Through dimensionless analysis, it was found that the intensity of this disturbance was directly proportional to the arithmetic square root of sound pressure and inversely proportional to frequency.

Image-domain material decomposition for dual-energy CT using unsupervised learning with data-fidelity loss.

Dual-energy computed tomography (DECT) and material decomposition play vital roles in quantitative medical imaging. However, the decomposition process may suffer from significant noise amplification, leading to severely degraded image signal-to-noise ratios (SNRs). While existing iterative algorithms perform noise suppression using different image priors, these heuristic image priors cannot accurately represent the features of the target image manifold. Although deep learning-based decomposition methods have been reported, these methods are in the supervised-learning framework requiring paired data for training, which is not readily available in clinical settings. This work aims to develop an unsupervised-learning framework with data-measurement consistency for image-domain material decomposition in DECT. The proposed framework combines iterative decomposition and deep learning-based image prior in a generative adversarial network (GAN) architecture. In the generator module, a data-fidelity loss is introduced to enforce the measurement consistency in material decomposition. In the discriminator module, the discriminator is trained to differentiate the low-noise material-specific images from the high-noise images. In this scheme, paired images of DECT and ground-truth material-specific images are not required for the model training. Once trained, the generator can perform image-domain material decomposition with noise suppression in a single step. In the simulation studies of head and lung digital phantoms, the proposed method reduced the standard deviation (SD) in decomposed images by 97% and 91% from the values in direct inversion results. It also generated decomposed images with structural similarity index measures (SSIMs) greater than 0.95 against the ground truth. In the clinical head and lung patient studies, the proposed method suppressed the SD by 95% and 93% compared to the decomposed images of matrix inversion. Since the invention of DECT, noise amplification during material decomposition has been one of the biggest challenges, impeding its quantitative use in clinical practice. The proposed method performs accurate material decomposition with efficient noise suppression. Furthermore, the proposed method is within an unsupervised-learning framework, which does not require paired data for model training and resolves the issue of lack of ground-truth data in clinical scenarios.