Background Noise Research Articles

Ambient Noise‐Derived SmS Splitting: A New Approach to Constraining Crustal Radial Anisotropy

AbstractRecent studies have shown that crustal body wave phases, such as PmP or SmS, can be effectively retrieved from ambient noise cross‐correlations. However, few studies have used these phases to constrain crustal structures. In this study, we successfully retrieve SmS signals from ambient noise data and observe SmS splitting caused by crustal radial anisotropy. Furthermore, through simulations of synthetic tests and application to field data, we demonstrate that these SmS signals can be used to constrain crustal radial anisotropy structures through joint inversion with surface waves. Our findings suggest that SmS signals obtained from noise data can significantly enhance the understanding of fine crustal radial anisotropic structures.

Novel bias-reduced coherence measure for EEG-based speech tracking in listeners with hearing impairment

In the literature, auditory attention is explored through neural speech tracking, primarily entailing modeling and analyzing electroencephalography (EEG) responses to natural speech via linear filtering. Our study takes a novel approach, introducing an enhanced coherence estimation technique to assess the strength of neural speech tracking. This enables effective discrimination between attended and ignored speech. To mitigate the impact of colored noise in EEG, we address two biases–overall coherence-level bias and spectral peak-shifting bias. In a listening study involving 32 participants with hearing impairment, tasked with attending to competing talkers in background noise, our coherence-based method effectively discerns EEG representations of attended and ignored speech. We comprehensively analyze frequency bands, individual frequencies, and EEG channels. Frequency bands of importance are shown to be delta, theta and alpha, and the important EEG channels are the central. Lastly, we showcase coherence differences across different noise reduction settings implemented in hearing aids (HAs), underscoring our method's potential to objectively assess auditory attention and enhance HA efficacy.

To Hear or Not to Hear: Cognitive Load Theory and Learning

ABSTRACT Background: Cognitive load is the amount of information one’s working memory can process at any given time. Key components of cognitive load include germane load, intrinsic load, and extraneous load. Learning is hindered if overload occurs, thus reducing cognitive performance. This study addresses the impact of auditory stimulation on cognitive load through repeated recognition tasks. Methods: Twenty-eight Queen’s University students were recruited and randomly allocated to control and experimental groups who completed the experiment with noise-cancelling headphones and with ambient noise, respectively. The experiment consisted of learning and testing phases. The learning phase involved memorizing a series of words. Subsequently, the testing phase required participants to recall words from the list. Each trial consisted of one learning and one testing phase, with three trials per participant. Cardiovascular measurements were taken prior to the experiment, at the beginning of each phase, and after each trial was completed. Results: Primary outcomes demonstrated that the experimental group experienced greater percent changes in heart rate and rate pressure product, higher perceived mental effort, and greater within-group variation for heart rate compared to controls. Scores decreased as trials progressed. Secondary outcomes showed that the top 33% of participants were less susceptible to changes in heart rate and blood pressure. Overall, four heart rate or blood pressure responders were identified. Conclusions: Exposure to 70 decibels of ambient noise contributes to cognitive load as noted by elevated cardiovascular measure and perceived mental effort. Results can inform educational platforms and hospital logistics.

Fluorescence microscopic image enhancement method based on multi-saliency guided filtering fusion

Abstract Aiming at addressing the issues of colour deviation and clarity reduction in digital pathological imaging systems caused by complex environmental interferences such as uneven slice thickness, sample jitter, and background noise, this paper proposes a multi-saliency-guided filtering fusion enhancement method that combines energy saliency and Gaussian saliency. Firstly, a series of Gamma correction and spatial linear saturation adjustments are applied to enhance the contrast and colour saturation of blurred images, resulting in a sequence of pseudo-exposure images with improved contrast and saturation. Then, the pseudo-exposure images are decomposed into base and detail layers, and the fusion rules of energy saliency and Gaussian saliency-guided filtering are applied to each layer separately. Finally, clear microscopic images are obtained through two-scale reconstruction. This method overcomes the limitations of conventional deblurring algorithms that rely on prior estimation of scene depth and complex depth mapping processes. Experimental results on a dataset of cytological bright-field microscopic imaging samples show that the clarity evaluation metrics, including peak signal-to-noise ratio, root mean square error, and structural similarity index, have significantly improved, indicating that the proposed method can effectively improve the colour fidelity of the samples while eliminating noise interference and enhancing the clarity of cellular texture structures.

Ambient noise surface-wave imaging in a hardrock environment: implications for mineral exploration

Summary The advancement of seismic methods is vital for mineral exploration in the ongoing energy transition. In this study, we investigate the application of ambient noise seismic interferometry and surface-wave analysis to characterize the subsurface in a mineral exploration context. We then confirm the results of the passive seismic investigation through an active source experiment. We collected ambient noise data using a 2D seismic line initially deployed for an active source reflection seismic study. By cross-correlating the signals, we retrieved the surface waves and constructed a 2D shear-wave velocity profile using conventional surface-wave analysis. We utilized the active source data to establish initial assumptions about the surveyed medium and then validated the passive seismic experiment. The passive seismic results are concordant with the active source results and allow for the interpretation of geological contacts and fault zones. Our work demonstrates the potential of passive seismic methods for investigating local tectonic settings and their role in hardrock mineral exploration.

Breathy Vocal Quality, Background Noise, and Hearing Loss: How Do These Adverse Conditions Affect Speech Perception by Older Adults?

Although breathy vocal quality and hearing loss are both prevalent age-related changes, their combined impact on speech communication is poorly understood. This study investigated whether breathy vocal quality affected speech perception and listening effort by older listeners. Furthermore, the study examined how this effect was modulated by the adverse listening environment of background noise and the listener's level of hearing loss. Nineteen older adults participated in the study. Their hearing ranged from near-normal to mild-moderate sensorineural hearing loss. Participants heard speech material of low-context sentences, with stimuli resynthesized to simulate original, mild-moderately breathy, and severely breathy conditions. Speech intelligibility was measured using a speech recognition in noise paradigm, with pupillometry data collected simultaneously to measure listening effort. Simulated severely breathy vocal quality was found to reduce intelligibility and increase listening effort. Breathiness and background noise level independently modulated listening effort. The impact of hearing loss was not observed in this dataset, which can be due to the use of individualized signal to noise ratios and a small sample size. Results from this study demonstrate the challenges of listening to speech with a breathy vocal quality. Theoretically, the findings highlight the importance of periodicity cues in speech perception in noise by older listeners. Breathy voice could be challenging to separate from the noise when the noise also lacks periodicity. Clinically, it suggests the need to address both listener- and talker-related factors in speech communication by older adults.

Imaging the Northern Los Angeles Basins with Autocorrelations

Abstract We show reflectivity cross sections for the San Gabriel, Chino, and San Bernardino basins north of Los Angeles (LA), California, determined from autocorrelations of ambient noise and teleseismic earthquake waves. These basins are thought to channel the seismic energy from earthquakes on the San Andreas fault to LA, and a more accurate model of their depth is important for hazard mitigation. We use the causal side of the autocorrelation function (ACF) to determine the zero-offset reflection response. To minimize the smoothing effect of the source time function, we remove the common mode from the autocorrelation to reveal the zero-offset reflection response. We apply this to 10 temporary nodal lines consisting of a total of 758 geophones with an intraline spacing of 250–300 m. We also show that the ACF from teleseismic events can provide illumination on the subsurface that is consistent with ambient noise. Both autocorrelation results compare favorably to receiver functions.

Feature Enhancement Based Oriented Object Detection in Remote Sensing Images

Since objects in remote sensing imagery often have arbitrary orientations and high densities, the features of small objects are inclined to be contaminated by the background and other instances. To address the issues, we propose a new oriented object detection framework where a series of feature enhancement schemes are implemented so as to improve robustness and accuracy of the detector. Firstly, we design a weighted bidirectional feature pyramid network, which can be used to fuse both high-level semantic features and low-level detail features for effectively handling with multi-scale objects. Accordingly, we apply the convolutional block attention module that exploits both spatial- and channel-wise attention in our detector, and study how to effectively integrate it into the framework for adaptive feature refinement. In the meanwhile, we present a semantic segmentation guided module to generate naive mask, which is used to multiple with pyramid features to filter out background noise and improve feature representation for small objects. The experimental results on two public datasets, i.e., UCAS-AOD and DOTA, validate the effective performance of the proposed method for oriented object detection in remote sensing images.

Noise test of an electromagnetic antenna for low frequency scattered gravitational waves

Abstract Gravitational waves (GW) proved hard to detect experimentally and yet carry high energy fluxes. Conversely the technology for electromagnetic waves (EMW) is sophisticated, easily available and highly sensitive. Various proposals have been made to use the conversion of a gravitational wave into an electromagnetic wave to generate signals that could be detected. We present test results from a novel electromagnetic antenna, efficient at the very low frequencies needed to explore the signals from supermassive black holes mergers. These will eventually be studied by the Laser Interferometric Space Antenna (LISA) at wavelengths between 108 and 1011 m. Common forms of electromagnetic antennae are only efficient when the ratio of wavelength, λ, to antenna length, L, is roughly between 0.1 and 10. This paper proposes a scheme for a realistic ground-based electromagnetic antenna , ”the resistive antenna”, which operates efficiently at a ratio $\frac{\lambda }{L}$ of more than 106. The first estimates of ambient noise at these low frequencies from field trials of such a device are presented. The measured levels of ambient noise result in this detection scheme having a sensitivity much worse than LISA. The antenna design described in this communication may also have application in other fields.

A Passive-Source Ocean-Bottom Seismic Experiment in the South China Sea: Evaluations of Data Quality and Instrument Performance

Abstract This study presents a passive-source ocean-bottom seismograph (OBS) experiment conducted in the southwest subbasin of the South China Sea (SCS) aimed at exploring the geodynamic processes shaping this short-lived oceanic basin. The successful deployment and recovery of 24 passive-source OBS units, including 14 I-7C types and 10 Pankun OBS units, resulted in the acquisition of one of the most extensive passive-source seismic data sets ever recorded in the SCS. We provide comprehensive details of the experiment, with a focus on evaluating the data quality and performance of the Pankun OBS units. This evaluation includes assessments of horizontal orientation determination, leveling system effectiveness, timing accuracy, and ambient noise spectrum. In addition, we compared the waveforms recorded by the Pankun OBS units with those from land stations and I-7C units, as well as the noise spectra between Pankun and global OBSs using the same seismometer. Although these comparisons suggest that the Pankun OBS achieves satisfactory performance, we identified a few inadequacies with this new instrument, such as issues with the differential pressure gauge, clock shifts, and sensor package calibration.

LightCardiacNet: light-weight deep ensemble network with attention mechanism for cardiac sound classification

Cardiovascular diseases (CVDs) account for about 32% of global deaths. While digital stethoscopes can record heart sounds, expert analysis is often lacking. To address this, we propose LightCardiacNet, an interpretable, lightweight ensemble neural network using Bi-Directional Gated Recurrent Units (Bi-GRU). It is trained on the PASCAL Heart Challenge and CirCor DigiScope datasets. Static network pruning enhances model sparsity for real-time deployment. We employ various data augmentation techniques to improve resilience to background noise. An ensemble of the two networks is constructed by employing a weighted average approach that combines the two light-weight attention Bi-GRU networks trained on different datasets, which outperforms several state-of-the-art networks achieving an accuracy of 99.8%, specificity of 99.6%, sensitivity of 95.2%, ROC-AUC of 0.974 and inference time of 17 ms on the PASCAL dataset, accuracy of 98.5%, specificity of 95.1%, sensitivity of 90.9%, ROC-AUC of 0.961 and inference time of 18 ms on the CirCor dataset, and an accuracy of 96.21%, sensitivity of 92.78%, specificity of 93.16%, ROC-AUC of 0.913 and inference time of 17.5 ms on real-world data. We adopt the SHAP algorithm to incorporate model interpretability and provide insights to make it clinically explainable and useful to healthcare professionals.

FireNet: A Lightweight and Efficient Multi-Scenario Fire Object Detector

Fire and smoke detection technologies face challenges in complex and dynamic environments. Traditional detectors are vulnerable to background noise, lighting changes, and similar objects (e.g., clouds, steam, dust), leading to high false alarm rates. Additionally, they struggle with detecting small objects, limiting their effectiveness in early fire warnings and rapid responses. As real-time monitoring demands grow, traditional methods often fall short in smart city and drone applications. To address these issues, we propose FireNet, integrating a simplified Vision Transformer (RepViT) to enhance global feature learning while reducing computational overhead. Dynamic snake convolution (DSConv) captures fine boundary details of flames and smoke, especially in complex curved edges. A lightweight decoupled detection head optimizes classification and localization, ideal for high inter-class similarity and small targets. FireNet outperforms YOLOv8 on the Fire Scene dataset (FSD) with a mAP@0.5 of 80.2%, recall of 78.4%, and precision of 82.6%, with an inference time of 26.7 ms. It also excels on the FSD dataset, addressing current fire detection challenges.

Noise analysis of a quasi-phase-matched quantum frequency converter and higher-order counter-propagating SPDC

Quantum frequency conversion (QFC) will be an indispensable ingredient in future quantum technologies. For example, large-scale fibre-based quantum networks will require QFC to interconnect heterogeneous building blocks like emitters, channels, memories, and detectors. The performance of existing QFC devices – typically realized in periodically poled nonlinear crystals – is often severely limited by parasitic noise that arises when the pump wavelength lies between the inter-converted wavelengths. Here we comprehensively investigate the noise spectrum of a QFC device pumped by a CW 1064 nm laser. The converter was realized as a bulk periodically poled potassium titanyl phosphate (ppKTP) crystal quasi-phase-matched for conversion between 637 nm and 1587 nm, which was also polished and coated to resonantly enhance the pump field by a factor of 50. While Raman scattering dominates the noise background from 1140 nm to 1330 nm, at larger energy shifts (beyond 60 THz), parasitic spontaneous parametric down-conversion (SPDC) is the strongest noise source. Further, the noise spectrum was contaminated by a regular succession of narrow-band peaks, which we attribute to a heretofore unidentified higher-order counter-propagating SPDC processes – with quasi-phase-matching orders up to 44 evident in our measurements. This work provides a comprehensive overview of the limiting noise sources in QFC devices that use quasi-phase-matched crystals and will prove an invaluable resource in guiding their future development.

Active Impulsive Noise Control with Missing Input Data Based on FxImdMCC Algorithm

In this study, we address the challenge of noise reduction in environments characterized by impulsive noise and missing input data in active noise control (ANC) systems, where existing algorithms often fail to deliver satisfactory results. Background noise, especially impulsive noise, poses a significant obstacle to signal processing and noise reduction. Furthermore, data loss during transmission or acquisition further complicates the noise reduction task. In this paper, a filtered-x imputation of the missing data maximum correntropy criterion (FxImdMCC) algorithm is proposed based on an imputation model, least mean square, and the maximum correntropy criterion (MCC), which can effectively reduce the impact of outliers and missing input data. The simulation results demonstrate the efficacy of the proposed FxImdMCC algorithm, which significantly outperforms existing algorithms in the context of active impulsive noise control.

Deep geophysical investigations (DERT and Seismic Reflection) to unravel the Ferrara urban area geology

Exploring the ‘fragile crust of our planet’ is crucial for human survival holding an immense social and economic significance. Therefore, innovative approaches become of utmost importance for obtaining precise subsoil models in urban areas making the latter more resilient to natural disasters. Due to logistic issues and a high level of anthropogenic disturbance and related background noise, urban areas are usually intrinsically more problematic for applying geophysical prospecting methods. This work presents the results obtained by Deep Electrical Resistivity Tomography and P wave Seismic Reflection surveys performed along the Ferrara, north Italy, city walls documenting the adaptability of the geophysical surveys and how it is possible to obtain high-quality electrical resistivity and seismic data even in complex urban settings. The joint interpretation of geoelectrical and seismic data fully integrated with tectonic, geological and hydrogeological information allowed to reconstruct the stratigraphic evolution down to a depth of about 1.5 km. These results highlight the occurrence of a syndepositional Quaternary tectonic tilting associated with the growth of a fault-propagation fold.

Recognition and classification of noise signals by dolphins under conditions of noise interference and spatial uncertainty of their simultaneous presentation

The ability of dolphin`s auditory system to recognize and classify noise signals according to certain invariant features under the influence of noise interference and spatial uncertainty of their simultaneous presentation was studied. The bottlenose dolphins trained to differentiate such signals had to solve this problem under conditions simulating real marine ones, when the perception of a useful signal occurs against the background of similar signals and noise interference. First, the noise signals were sequentially presented to the animal against the background of white masking noise. Further the dolphin had to identify a signal of a positive class from several simultaneously sounding sound sources. The efficiency of the animal was evaluated at several given levels of noise interference. In this case, the actual noise interference was both white noise and simultaneously sounding negative signals. It`s shown that the efficiency and noise immunity of dolphin`s auditory system depend on the degree of alternativeness of the spatial uncertainty of the simultaneous presentation of signals.

Improving ambient noise crosscorrelations using a polarization-based azimuth filter

Seismic interferometry is widely used to image and monitor earth structures at different scales by extracting an empirical Green’s function (EGF) from the crosscorrelation of ambient noise under the assumption of diffuse wavefields or energy equipartitioning. However, EGFs may be incorrectly estimated and lead to a misunderstanding of subsurface structures because the distribution of ambient noise sources is neither isotropic nor stationary. To extract reliable EGFs from the nonideal ambient noise data, we develop a polarization-based azimuth filter (PAF) to attenuate the ambient noise energy of nonstationary sources to improve the quality of crosscorrelation functions. The PAF is a time-frequency domain azimuth filter for 3C seismic data, which uses time-frequency polarization analysis to measure the azimuths of ambient noise signals and then filters the 3C data to obtain the signals from desired directions. Based on the stationary-phase theory, we use the PAF to extract the ambient noise signals from stationary-phase zones, which improves the quality of the crosscorrelation function and eliminates the requirement for long observations. A synthetic test and two short-time engineering examples demonstrate that uneven source distributions might cause serious spurious signals in EGFs, and we demonstrate the improvement in EGFs using our method. In addition, the PAF may allow for the complete separation of the fundamental and higher modes of Rayleigh waves, which uncovers more information implicit in the ambient noise data.

Multichannel Analysis of Surface Waves based on Common Virtual Source Gathers of Seismic Ambient Noise Cross-Correlations: A Case Study at an Earth Dam in Brazil

The S-wave velocity (Vs) is a valuable parameter for assessing the mechanical properties of subsurface materials for geotechnical purposes. Seismic surface wave methods have become prominent for estimating near-surface Vs models. Researchers have proposed methods based on passive seismic signals as efficient alternatives to enhance depth of investigation, lateral resolution and reduce field effort. This study presents the Multichannel Analysis of Surface Waves (MASW) utilizing Common Virtual Source Gathers (CVSGs) derived from seismic ambient noise cross-correlations, based on Ambient Noise Seismic Interferometry concepts. The method is applied to passive data acquired with an array of receivers at the Paranoá earth dam in Brasília, Brazil, to construct a pseudo-2D Vs image of the massif for interpretation. Our findings showcase the adopted processing flow and combination of methods as an effective approach for near-surface Vs estimation, demonstrating its usability also for large earth dam embankments.

Adaptive Threshold Wave Front Sensing for Portable Adaptive Optics System Driven by Hybrid Parallel Technique

Our Portable Adaptive Optics (PAO) system designed for high-contrast imaging of exoplanets with current 2–4 m class telescopes achieves a correction speed of nearly 1000 Hz, utilizing a Shack–Hartmann Wave Front Sensor (WFS) in a 9 × 9 sub-aperture configuration. As we look towards adapting the PAO system for larger telescopes, an increase in the number of sub-apertures in the WFS and enhanced precision in wave front detection are imperative. Originally programmed in LabVIEW, our initial PAO software is based on a traditional centroid calculation module for nighttime wave front sensing and lacks adaptive processing of background noise. To address these limitations and to boost the PAO system's performance and accuracy in wave front detection, we propose a compressive neural network (Th-Net) combined with a specialized hybrid parallel programming approach for wave front detection. Our experimental results indicate that this hybrid parallel technique and Th-Net significantly enhance the PAO system's operational speed and wave front detection precision under uneven background noise. This work paves the way so that a duplicable and low-cost PAO system can be used for direct imaging of exoplanets with large telescopes.

Development of a Key Method for the Optimization of Port Vessel Detection Based on an Improved Multi-Structural Morphology Approach

All over the world, many ports have implemented surveillance camera systems to monitor the vessels and activities around them. These types of systems are not very effective in accurately detecting activities around the port due to background noise and congestion interference at the sea surface. This is why it is difficult to accurately detect vessels, especially smaller vessels, when it turns dark. It is known that some vessels do not comply with maritime rules, particularly in port and safety zones; these must be detected to avoid incidents. For these reasons, in this study, we propose and develop an improved multi-structural morphology (IMSM) approach to eliminate all of this noise and interference so that vessels can be accurately detected in real time. With this new approach, the target vessel is separated from the sea surface background through the weighted morphological filtering of several datasets of structural components. Then, neighborhood-based adaptive fast median filtering is used to filter out impulse noise. Finally, a characteristic morphological model of the target vessel is established using the connected domain; this allows the sea surface congestion to be eliminated and the movement of vessels to be detected in real time. Multiple tests are carried out on a small and discrete area of moving vessels. The results from several collected datasets show that the proposed approach can effectively eliminate background noise and congestion interference in video monitoring. The detection accuracy rate and the processing time are improved by approximately 3.91% and 1.14 s, respectively.