Background Noise Research Articles

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 Fused Deep Denoising Sound Coding Strategy for Bilateral Cochlear Implants.

Cochlear implants (CIs) provide a solution for individuals with severe sensorineural hearing loss to regain their hearing abilities. When someone experiences this form of hearing impairment in both ears, they may be equipped with two separate CI devices, which will typically further improve the CI benefits. This spatial hearing is particularly crucial when tackling the challenge of understanding speech in noisy environments, a common issue CI users face. Currently, extensive research is dedicated to developing algorithms that can autonomously filter out undesired background noises from desired speech signals. At present, some research focuses on achieving end-to-end denoising, either as an integral component of the initial CI signal processing or by fully integrating the denoising process into the CI sound coding strategy. This work is presented in the context of bilateral CI (BiCI) systems, where we propose a deep-learning-based bilateral speech enhancement model that shares information between both hearing sides. Specifically, we connect two monaural end-to-end deep denoising sound coding techniques through intermediary latent fusion layers. These layers amalgamate the latent representations generated by these techniques by multiplying them together, resulting in an enhanced ability to reduce noise and improve learning generalization. The objective instrumental results demonstrate that the proposed fused BiCI sound coding strategy achieves higher interaural coherence, superior noise reduction, and enhanced predicted speech intelligibility scores compared to the baseline methods. Furthermore, our speech-in-noise intelligibility results in BiCI users reveal that the deep denoising sound coding strategy can attain scores similar to those achieved in quiet conditions.

A Label-Free Photoelectrochemical Biosensor Based on ZnO/Cs3MnBr5 Heterogeneous Films for Alpha-Fetoprotein Detection

Highly sensitive and specific biomarker detection is of outstanding importance for the diagnosis and treatment of cancers. Herein, we developed robust photoelectrochemical (PEC) biosensors with low background noise and high sensitivity based on a heterojunction, which can improve semiconductor photoelectric properties by limiting the recombination of photogenerated electron–hole pairs and successfully widening the range of light absorption. Alpha-fetoprotein (AFP) was used as a target model to examine the analytical performances of the designed PEC biosensors. ZnO/Cs3MnBr5 heterogeneous film with a uniform porous structure and large surface area enhanced electron transfer and biomolecule immobilization, and significantly increased the photocurrent response. Under the optimal conditions, the designed PEC biosensor exhibited a linear detection range of 0.01–500 ng/mL and a detection limit of 12 pg/mL. In addition, this PEC biosensor performed well when testing human serum samples and exhibited good repeatability, stability over time, and specificity, showing enormous potential for the detection of cancer markers in future biological and clinical research.

Periodic group-sparse method via generalized minimax-concave penalty for machinery fault diagnosis

Abstract Diagnosing faults in large mechanical equipment poses challenges due to strong background noise interference, wherein extracting weak fault features with periodic group-sparse property is the most critical step for machinery intelligent maintenance. To address this problem, a periodic group-sparse method based on a generalized minimax-concave penalty function is proposed in this paper. This method uses periodic group sparse techniques to capture the periodic clustering trends of fault impact signals. To further enhance the sparsity of the results and preserve the high amplitude of the impact signals, non-convex optimization techniques are integrated. The overall convexity of the optimization problem is maintained through the introduction of a non-convex controllable parameter, and an appropriate optimization algorithm is derived. The effectiveness of this method has been demonstrated through experiments with simulated signals and mechanical fault signals.

Ambient noise tomography reveals complex crustal structure beneath eastern and northeastern Tibet: Insight‐ into regional crustal flow

Ambient noise tomography reveals complex crustal structure beneath eastern and northeastern Tibet: Insight‐ into regional crustal flow

Automated crack localization for road safety using contextual u-net with spatial-channel feature integration

Accurate and timely crack localization is crucial for road safety and maintenance, but image processing and hand-crafted feature engineering methods, often fail to distinguish cracks from background noise under diverse lighting and surface conditions. This paper proposes a framework utilizing contextual U-Net deep learning model to automatically localize cracks in road images. The framework design considers crack localization as a task of pixel-level segmenting, and analyzing each pixel in a road image to determine if it belongs to a crack or not. The proposed U-Net model uses a robust EfficientNet encoder to capture crucial details (spatial features) and overall patterns (channel-wise features) within the road image. This balanced approach helps the model learn effectively from both individual elements and the context of the images, leading to improved crack detection. A customized hierarchical attention mechanism is designed to make U-Net model contextually adaptive to focus on relevant features at different scales and resolutions for accurately localizing road cracks that can vary widely in size and shape. The model's effectiveness is demonstrated through extensive evaluations on the benchmarked and custom-made datasets.

Effect of manual OCTA segmentation correction to improve image quality and visibility of choroidal neovascularization in AMD

In this retrospective case series on neovascular age-related macular degeneration (nAMD), we aimed to improve Choroidal Neovascularization (CNV) visualization in Optical Coherence Tomography Angiography (OCTA) scans by addressing segmentation errors. Out of 198 eyes, 73 OCTA scans required manual segmentation correction. We compared uncorrected scans to those with minimal (2 corrections), moderate (10 corrections), and detailed (50 corrections) efforts targeting falsely segmented Bruch’s Membrane (BM). Results showed that 55% of corrected OCTAs exhibited improved quality after manual correction. Notably, minimal correction (2 scans) already led to significant improvements, with additional corrections (10 or 50) not further enhancing expert grading. Reduced background noise and improved CNV identification were observed, with the most substantial improvement after two corrections compared to baseline uncorrected images. In conclusion, our approach of correcting segmentation errors effectively enhances image quality in OCTA scans of nAMD. This study demonstrates the efficacy of the method, with 55% of resegmented OCTA images exhibiting enhanced quality, leading to a notable increase in the proportion of high-quality images from 63 to 83%.

Monitoring urban construction and quarry blasts with low-cost seismic sensors and deep learning tools in the city of Oslo, Norway

The aim of this study is to collect information about events in the city of Oslo, Norway, that produce a seismic signature. In particular, we focus on blasts from the ongoing construction of tunnels and under-ground water storage facilities under populated areas in Oslo. We use seismic data recorded simultaneously on up to 11 Raspberry Shake sensors deployed between 2021 and 2023 to quickly detect, locate, and classify urban seismic events. We present a deep learning approach to first identify rare events and then to build an automatic classifier from those templates. For the first step, we employ an outlier detection method using auto-encoders trained on continuous background noise. We detect events using an STA/LTA trigger and apply the auto-encoder to those. Badly reconstructed signals are identified as outliers and subsequently located using their surface wave (Rg) signatures on the seismic network. In a second step, we train a supervised classifier using a Convolutional Neural Network to detect events similar to the identified blast signals. Our results show that up to 87% of about 1,900 confirmed blasts are detected and locatable in challenging background noise conditions. We demonstrate that a city can be monitored automatically and continuously for explosion events, which allows implementing an alert system for future smart city solutions.

Iterative Laplace of Gaussian filter and improved Teager energy operator for bearing fault detection in gearboxes

Abstract The presence of cyclic impulses is considered to be a significant indicator of bearing defects. The extraction of fault-related information from gearboxes, however, poses a significant challenge due to the complex working conditions in gear transmission systems. These conditions often lead to fault-related impulse features being masked by inevitable background noise and vibration components. To address this issue, a novel iterative Laplace of Gaussian (LoG) filtering technique is proposed for the fault diagnosis field of bearings in gearboxes. An objective function incorporating Kurtosis statistics is used to iteratively update LoG coefficients instead of relying on fixed coefficients as in the original version, making the iterative LoG filtering technique better suited for handling fault-induced impact signals. Furthermore, an improved Teager energy operator (TEO) is developed to reduce the susceptibility of the original TEO to noise. The improvement involves incorporating a multi-resolution symmetric difference sequence into the discrete expression of TEO. This symmetric difference acts as a translation-invariant, sliding-window filter that, when combined with an energy operator, enables both energy detection and frequency filtering. The robustness against noise and sensitivity to frequency can be further improved by adjusting the multi-resolution parameter. The results of the experiments confirm the effectiveness of the proposed approach in identifying weak bearing fault features for gearboxes. Besides, the proposed method is also compared with the original LoG filter-based method and some competing methods, demonstrating its superior performance.

Sliding time synchronous averaging based on independent extended autocorrelation function for feature extraction of bearing fault

Extracting fault feature is a key challenge during the early failure stage of bearings due to weak fault components and strong background noises. To solve this problem, an extension operation is introduced, and a novel algorithm called independent extended autocorrelation function is proposed. Subsequently, based on time synchronous averaging, the sliding time synchronous averaging method is developed to further enhance periodic components. The time dimension is added to generate a series of periodic impulses, which is advantageous to directly extract the fault characteristic. Therefore, the presented algorithm is called the sliding time synchronous averaging based on independent autocorrelation function (STSA-IEACF). Numerically simulated signals and two experimental datasets are employed to compare the performance of the STSA-IEACF with the mainstream blind deconvolution methods in fault feature extraction of bearing. The results show that the proposed algorithm performs better than the others in this terms of extracting fault characteristics.

Spatial and temporal variation in ambient vibratory noise and its impact potential on a common urban arthropod

Spatial and temporal variation in ambient vibratory noise and its impact potential on a common urban arthropod

Maximum Gpq–mean deconvolution for the impulsive fault feature enhancement of rolling bearing

Abstract The bearing fault signal is easily obscured by background noise and random shocks in the initial stage. The maximum Gpq–mean deconvolution (MGD) method is proposed to address the challenge of extracting fault feature signals in the presence of impact interference. The use of a nonlinear activation function in MGD enhances the distribution characteristics of the filtered signal. The proposed method adopts a new sparse measurement method, which enhances the sparse measurement capability and solves the problem of the difficulty in extracting periodic fault signals under impact. The superiority of the method in rolling bearing diagnosis is demonstrated through simulation and experimental analyses. In comparison with traditional methods, such as minimum entropy deconvolution (MED), optimal minimum entropy deconvolution adjustment (OMEDA), and maximum correlated kurtosis deconvolution (MCKD), the proposed method in this paper significantly improves the ability of extracting bearing fault signals.
Keywords: Minimum Entropy Deconvolution; Kurtosis; Blind Deconvolution; Sparse Metric

PSO-optimized SSLMS adaptive filter for signal denoising of rolling bearings under small sample condition

Abstract To address the issue that the deep learning-based denoising algorithms can hardly effectively eliminate the background noise under small sample data condition, this paper proposes a new denoising method based on spectral subtraction (SS) and least mean square (LMS) adaptive filtering algorithms. To achieve the adaptive selection for the parameters of SS and LMS algorithms, particle swarm optimization (PSO) approach is employed to search and optimize the parameters in the two algorithms, which is helpful for the two algorithms to play an important role in eliminating the noise components with the different properties. Subsequently, the SS algorithm and the LMS algorithm are appropriately combined, and the SS-processed signal is input into the LMS algorithm as a desired signal to actualize the LMS adaptive filtering function. In this way, the denoising performance of both algorithms can be maximally utilized, which achieves effective noise reduction in vibration signal. The effectiveness and superiority of the proposed method are validated through simulation data and rolling bearing experiment data, respectively. The results demonstrate that the proposed method significantly diminishes noise components and retains precise and reliable fault features under small sample data condition, which provides an effective denoising method for rolling bearing vibration signals under small sample data condition in practical engineering scenarios.

Quantum double lock-in amplifier

Quantum lock-in amplifiers have been proposed to extract an alternating signal from a strong noise background. However, due to the typical target signal has unknown initial phase, it is challenging to extract complete information about the signal’s amplitude, frequency, and initial phase. Here, we present a general protocol for achieving a quantum double lock-in amplifier by employing two quantum mixers operating under orthogonal pulse sequences. To demonstrate the practical implementation, we discuss the experimental feasibility using a five-level double-Λ coherent population trapping system with Rb atoms. Here, each Λ structure acts as a quantum mixer, and two applied dynamical decoupling sequences serve as orthogonal reference signals. Notably, the system significantly reduces the total measurement time by nearly half and mitigates time-dependent systematic errors compared to conventional two-level systems. Furthermore, our quantum double lock-in amplifier is robust against experimental imperfections. This study establishes a pathway to alternating signal measurement, thereby facilitating the development of practical quantum sensing technologies.

A seismic ambient noise data classification method based on waveform and frequency-wavenumber analysis: Application to reliable geological interpretation adjacent to Well Songke-2, Northeast China

Passive seismic interferometry techniques enable the retrieval of virtual shot gathers at receiver locations. However, it is difficult to distinguish body waves from surface waves in seismic ambient noise data, which leads to failed dispersion measurements, artificial deep reflections, and misleading geologic interpretations. We develop a new method that uses the waveforms and their corresponding f- k spectra to identify body waves and surface waves. Our technique functions as a data classification procedure before wavefield retrieval and subsequent imaging. A field data example is conducted to test the feasibility of this technique. The field data results demonstrate that our technique can reconstruct satisfactory high signal-to-noise-ratio wavefields for reliable imaging and interpretation purposes. The method can also serve as a validation tool for identifying surface wave-related artificial events in the obtained virtual source reflection image. The marker boundaries of the Songliao Basin in Northeast China are interpreted in the obtained profile and are in agreement with the well-logging data. In addition, two normal faults are detected, which correspond to the extensional rift environment during the early Cretaceous.

Metasurface Platform Incorporating Aggregation Induced Emission Based Biosensor for Enhanced Human Serum Albumin Detection

AbstractMetasurfaces exhibit excellent optical performance to enhance the light–matter interaction of target molecules in biosensing based on its well‐optimized nanostructured unit cells. In the meantime, fluorescence (FL) biosensors with aggregation induced emissions (AIE) features also demonstrate outstanding performance in biomarker detection due to their fast response, high selectivity, and low background noise. Nevertheless, extremely low‐level analytes are difficult to detect in practical applications since complex urine samples include a number of uncontrolled variables such as impurities, autofluorescence, other urine components, etc. At present, improving optical signal sensitivity of human serum albumin (HSA) detection is always a big challenge to overcome such interference in human urine scenarios. In this work, first an AIE‐based FL biosensor TPE‐4TA is combined with an all‐dielectric metasurface platform to achieve quantitative detection of trace HSA in urine by utilizing biofunctionalization protocols on the silicon (Si) nanostructures. The results indicate significant FL enhancement in the metasurface platform that offers a promising pathway for improving biomarker detection in the future.

AeroDetectNet: a lightweight, high-precision network for enhanced detection of small objects in aerial remote sensing imagery

Object detection in remote sensing imagery exhibits difficulties due to complex backgrounds, diverse object scales, and intricate spatial context relationships. Motivated by the problems mentioned above, this paper introduces AeroDetectNet, a novel lightweight and high-precision object detection network custom-designed for aerial remote sensing scenarios, building upon the YOLOv7-tiny algorithm. It enhances performance through four key improvements: the normalized Wasserstein distance for consistent object size sensitivity, the Involution module for reduced background noise, a self-designed RCS-biformer module for better spatial context interpretation, and a self-designed WF-CoT SPPCSP feature pyramid for improved feature map weighting and context capture. Ablation studies conducted on a hybrid dataset composed of three open-source remote sensing datasets (including NWPU VHR-10 remote sensing images, RSOD remote sensing images, and VisDrone UAV images) have demonstrated the effectiveness of four improvements specifically for small-size object detection. Visualizations through Grad-CAM further demonstrate AeroDetectNet’s capacity to extract and focus on key object features. Upon individual testing across three open-source datasets, AeroDetectNet has successfully demonstrated its ability to identify objects in images with a smaller pixel area. Through experimental comparisons with other related studies, the AeroDetectNet achieved a competitive mAP while maintaining fewer model parameters, highlighting its highly accurate and lightweight properties.

Feasibility of acousto-electric tomography

In acousto-electric tomography (AET) the goal is to reconstruct the electric conductivity in a domain from electrostatic boundary measurements of corresponding currents and voltages, while the domain is perturbed by a time-dependent acoustic wave, thus taking advantage of the acousto-electric effect. We approach the AET reconstruction in two steps: First, the interior power density is obtained from boundary measurements by solving a linear inverse and ill-posed problem; second, the interior conductivity is reconstructed from the power density by solving a non-linear and well-posed problem. Mathematically these inverse problems are fairly well understood, and reconstruction methods work well on synthetic data. This is in contrast to experimental findings. An effect can indeed be observed and data can be collected. However, the acousto-electric coupling is very weak, and consequently, the change in the measured voltage due to the acoustic perturbation might be too small compared to the background noise for viable reconstructions. In this paper, we take one step towards understanding the feasibility of AET. We provide an in-silico model of the coupled physics scenario based on standard models for the individual phenomena. Moreover, we formulate and implement numerically a full reconstruction method for the inverse problem via the two steps. We perform computational experiments with realistically chosen parameters from the context of medical imaging. The focus is on understanding the role of the acousto-electric coupling parameter and the signal-to-noise ratio (SNR). The critical signal strength is analyzed and the omnipresent Johnson–Nyquist noise is estimated. We obtain both positive and negative findings; we can reconstruct features even under severe noise conditions, but we also find that the SNR one is likely to face in practice is too low to obtain useful reconstructions.

Underwater acoustic signal denoising based on sparse TQWT and wavelet thresholding

The time-varying characteristics of the underwater environment lead to complicated background noise in collected signals. To reduce the negative impact of noise, this paper proposes the tunable Q-factor wavelet transform (TQWT)-basis pursuit (TQWT-BP)-based wavelet thresholding denoising schemes (TQWT-BP-WT). We commence by decomposing the collected signal into multiple subbands upon using TQWT-BP. Then, subbands are divided into signal subbands and noise subbands by applying the component identification criteria. The wavelet thresholding is invoked to denoise the signal subbands, while noise subbands are discarded. To classify the subbands, we design two criteria based on the coefficients threshold and the correlation coefficients, respectively. The coefficients threshold-based criterion determines the subband components by examining the number of coefficients that satisfy the threshold conditions. By comparing the correlation coefficients between the subbands and original signal, as well as between the subbands and the pre-denoised signal, the correlation coefficient-based method distinguishes the subbands, which avoids setting the criterion threshold. In addition, we investigate the influence of different normalized signal amplitudes decomposed by TQWT-BP, and the design of the normalized scale selection approach. Finally, practical simulation results are provided to verify the performance of our proposed schemes compared to other counterparts.

Research on image restoration of [formula omitted] porous foam ceramics media using Transformer architecture

Pore structure images of foam ceramics play a crucial role in the field of refractory materials science and engineering, facilitating the observation and analysis of the material’s pore structure and unique characteristics. However, traditional image restoration techniques have limitations in handling background noise, complex texture restoration, deformation, and dislocation correction, as well as automation and intelligence aspects in restoring material pore structure images. The latest Transformer architecture has shown significant potential in image processing, particularly in image restoration research tasks. Nevertheless, its application in restoring pore structure images of materials remains unexplored. This paper aims to address this gap by focusing on the image restoration of foam ceramic pore structure images using a Transformer-based algorithm for super-resolution (SR) processing. Experimental comparisons with state-of-the-art methods demonstrate a remarkable improvement in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM), underscoring the effectiveness of the proposed approach.