Ambient Background Noise Research Articles

Simulated Hough Transform Model Optimized for Straight-Line Recognition Using Frontier FPGA Devices

The use of the Hough transforms to identify shapes or images has been extensively studied in the past using software for artificial intelligence applications. In this article, we present a generalization of the goal of shape recognition using the Hough transform, applied to a broader range of real problems. A software simulator was developed to generate input patterns (straight-lines) and test the ability of a generic low-latency system to identify these lines: first in a clean environment with no other inputs and then looking for the same lines as ambient background noise increases. In particular, the paper presents a study to optimize the implementation of the Hough transform algorithm in programmable digital devices, such as FPGAs. We investigated the ability of the Hough transform to discriminate straight-lines within a vast bundle of random lines, emulating a noisy environment. In more detail, the study follows an extensive investigation we recently conducted to recognize tracks of ionizing particles in high-energy physics. In this field, the lines can represent the trajectories of particles that must be immediately recognized as they are created in a particle detector. The main advantage of using FPGAs over any other component is their speed and low latency to investigate pattern recognition problems in a noisy environment. In fact, FPGAs guarantee a latency that increases linearly with the incoming data, while other solutions increase latency times more quickly. Furthermore, HT inherently adapts to incomplete input data sets, especially if resolutions are limited. Hence, an FPGA system that implements HT is inefficient for small sets of input data but becomes more cost-effective as the size of the input data increases. The document first presents an example that uses a large Accumulator consisting of 1100 × 600 Bins and several sets of input data to validate the Hough transform algorithm as random noise increases to 80% of input data. Then, a more specifically dedicated input set was chosen to emulate a real situation where a Xilinx UltraScale+ was to be used as the final target device. Thus, we have reduced the Accumulator to 280 × 280 Bins using a clock signal at 250 MHz and a few tens input points. Under these conditions, the behavior of the firmware matched the software simulations, confirming the feasibility of the HT implementation on FPGA.

A Class of Pareto Optimal Binaural Beamformers

The objective of binaural multi-microphone speech enhancement algorithms can be viewed as a multi-criteria design problem as there are several requirements to be met. The objective is not only to extract the target speaker without distortion, but also to suppress interfering sources (e.g., competing speakers) and ambient background noise, while preserving the auditory impression of the complete acoustic scene. Such a multi-objective problem (MOP) can be solved using a Pareto frontier, which provides a useful trade-off between the different criteria. In this paper, we propose a unified Pareto optimization framework, which is achieved by defining a generalized mean squared error (MSE) cost function, derived from a MOP. The solution to the multi-criteria problem is grounded on a solid mathematical foundation. The MSE cost function consists of a weighted sum of speech distortion (SD), partial interference reduction (IR), and partial noise reduction (NR) terms with scaling parameters that control the amount of IR and NR. The filter minimizing this generalized cost function, denoted Pareto optimal binaural multichannel Wiener filter (Pareto-BMWF), constitutes a generalization of various binaural MWF-based and binaural MVDR-based beamformers. This solution is optimal for any set of parameters. The improved speech enhancement capabilities are experimentally demonstrated using real-signal recordings when estimation errors are present and the binaural cue preservation capabilities are analyzed.

Design and Implementation of Ultra-Wide Band Antenna for Partial Discharge Detection in High Voltage Power Equipment

Partial discharges (PD) are the most common and harmful threat to the health of electrical insulation of high voltage (HV) and high field (HF) equipment. The occurrence of PD activity in HV and MV equipment is at the same time a cause and a sign of insulation degradation that may eventually result in the breakdown of the HV power equipment. For the safe and reliable operation of HV power equipment, continuous PD monitoring needs to be conducted conveniently to prevent any unplanned power outages and damage to electrical power equipment. The ultra-high frequency (UHF) PD measurement method has been widely used as an effective technique to detect PD activity on HV power equipment due to its non-invasive principle. To enable PD detection accuracy, there is still a need for more sensitive PD sensors that can assist the UHF PD monitoring system by suppressing ambient background noise and low-frequency electromagnetic interferences from telecommunication such as GSM signals. To tackle the sensitivity issues, this article presents a new design of ultra-wideband (UWB) microstrip patch antenna that is capable of effective suppression of low-frequency interference signals. The proposed antenna, designed, simulated, and optimized using the CST Microwave Studio software, has a bandwidth of 3.3GHz, below -10dB, in the operating frequency range of 1.2GHz-4.5GHz. The prototype of this antenna, printed on an FR4 substrate of a thickness of 1.6mm and a dielectric constant of 4.4, featuring a compact size of 100mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times100$ </tex-math></inline-formula> mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times1.6$ </tex-math></inline-formula> mm, was implemented to detect PD through laboratory experiments. This paper shows that the designed UWB antenna has high sensitivity, good noise rejection and it is, therefore, a promising candidate sensor for PD detection on HV equipment.

Introducing ISMDq—A Web Portal for Real-Time Quality Monitoring of Italian Strong-Motion Data

Abstract We present the Istituto Nazionale di Geofisica e Vulcanologia Strong-Motion Data-quality (ISMDq)—a new automatic system designed to check both continuous data stream and event strong-motion waveforms before online publication. The main purpose of ISMDq is to ensure accurate ground-motion data and derived products to be rapidly shared with monitoring authorities and the scientific community. ISMDq provides data-quality reports within minutes of the occurrence of Italian earthquakes with magnitude ≥3.0 and includes a detailed daily picture describing the performance of the target strong-motion networks. In this article, we describe and discuss the automatic procedures used by ISMDq to perform its data-quality check. Before an earthquake, ISMDq evaluates the selected waveforms through the estimation of quality indexes employed to reject bad data and/or to group approved data into classes of quality that are useful to quantify the level of reliability. The quality indexes are estimated based on comparisons with the background ambient noise level performed both in the time and frequency domains. As a consequence, new high- and low-noise reference levels are derived for the overall Italian strong-motion network, for each station, and for groups of stations in the same soil categories of the Eurocode 8 (Eurocode 8 [EC8], 2003). In absence of earthquakes, 24 hr streaming of ambient noise recordings are analyzed at each station to set an empirical threshold on selected data metrics and data availability, with the goal to build a station quality archive, which is daily updated in a time span of six months. The ISMDq is accessible online (see Data and Resources) from August 2020, providing rapid open access to ∼10,000 high-quality checked automatically processed strong-motion waveforms and metadata, relative to more than 160 Italian earthquakes with magnitude in the 3.0–5.2 range. Comparisons between selected strong-motion data automatically processed and then manually revised corroborate the reliability of the proposed procedures.

STUDY OF THE EFFECT OF MAGNETIC FIELDS ON ELECTROENCEPHALOGRAPHY MEASUREMENT IN FARADAY’S CAGE

Electroencephalography (EEG) is a method for recording the brain's electrical activity through electrodes placed on the scalp's surface. The amplitude of the EEG signal is in the 40–100 V range, with the five main frequencies in the 0 to 100 Hz range. The EEG is non-stationary and very susceptible to various disturbances, especially frequency disturbances, so eliminating troubles in the raw EEG data is essential to obtain helpful information reflecting brain activity. Interference in the EEG signal comes from muscles, eye movement and blinking, power lines, and interference with other devices. The distractions overlap. Shielding is required to perform an EEG without the risk of interference and ambient background noise. This study tested how the influence of magnetic field disturbances on EEG measurements was carried out in the Faraday cage and an unprotected room. The magnetic field was measured before, during, and after the EEG was operated. EEG measurements were performed on subjects who were conditioned to rest for 5 minutes. The EEG signals generated when EEG recordings were performed in the Faraday and the unprotected rooms were compared. It was found that the difference in the value of the magnetic field originating from electronic devices around the subject does not significantly affect the EEG measurement results.

Tagging ocean soundscapes with machine learning and topological approaches

With a multitude of sources simultaneously contributing to the ambient ocean soundscape, it is important to disaggregate noise source contributions for better monitoring and assessing noise impacts. Our work adopts a modular set of neural networks and topological pipelines to analyze multiple representations of time-series samples of the ambient ocean noise. Specifically, a convolutional neural network (CNN) is used to broadly tag the presence of local shipping and marine mammal vocalizations from spectrogram representations. Multiple approaches are investigated using topological data analysis to predict a class label of the ship hull type or specific marine mammal species using the audio data as input. The datasets used in training, testing, and validation comprise hydrophone recordings across varying sensors and ocean environments to address the wide variation in ambient background noise and propagation paths. Algorithm performance is characterized by classification accuracy on labeled data. This approach demonstrates a generalized tagging cabilitility of the presence of marine mammals and local shipping activity.

Detection of Respiratory Phases in a Breath Sound and Their Subsequent Utilization in a Diagnosis

Detection of lung sounds and their propagation is a powerful tool for analysing the behaviour of the respiratory system. A common approach to detect the respiratory sounds is lung auscultation, however, this method has significant limitations including low sensitivity of human ear or ambient background noise. This article targets the major limitations of lung auscultation and presents a new approach to analyse the respiratory sounds and visualise them together with the respiratory phases. The respiratory sounds from 41 patients were recorded and filtered to eliminate the ambient noise and noise artefacts. The filtered signal is processed to identify the respiratory phases. The article also contains an approach for removing the noise that is very difficult to filter but the removal is crucial for identifying the respiratory phases. Finally, the respiratory phases are overlaid with the frequency spectrum which simplifies the orientation in the recording and additionally offers the information on the inter-individual ratio of the inhalation and exhalation phases. Such interpretation provides a powerful tool for further analysis of lung sounds, simplifythe diagnosis of various types of respiratory tract dysfunctions, and returns data which are comparable among the patients.

The Deployment of the Seismometer to Investigate Ice and Ocean Structure (SIIOS) in Northwest Greenland: An Analog Experiment for Icy Ocean World Seismic Deployments

Abstract In anticipation of future spacecraft missions to icy ocean worlds, the Seismometer to Investigate Ice and Ocean Structure (SIIOS) was funded by National Aeronautics and Space Administration, to prepare for seismologic investigations of these worlds. During the summer of 2018, the SIIOS team deployed a seismic experiment on the Greenland ice sheet situated, approximately, 80 km north of Qaanaaq, Greenland. The seismometers deployed included one Trillium 120 s Posthole (TPH) broadband seismometer, 13 Silicon Audio flight-candidate seismometers, and five Sercel L28 4.5 Hz geophones. Seismometers were buried 1 m deep in the firn in a cross-shaped array centered on a collocated TPH and Silicon Audio instrument. One part of the array consisted of Silicon Audio and Sercel geophones situated 1 m from the center of the array in the ordinal directions. A second set of four Silicon Audio instruments was situated 1 km from the center of the array in the cardinal directions. A mock-lander spacecraft was placed at the array center and instrumented with four Silicon Audio seismometers. We performed an active-source experiment and a passive-listening experiment that lasted for, approximately, 12 days. The active–source experiment consisted of 9–12 sledgehammer strikes to an aluminum plate at 10 separate locations up to 100 m from the array center. The passive experiment recorded the ice-sheet ambient background noise, as well as local and regional events. Both datasets will be used to quantify differences in spacecraft instrumentation deployment strategies, and for evaluating science capabilities for single-station and small-aperture seismic arrays in future geophysical missions. Our initial results indicate that the flight-candidate seismometer performs comparably to the TPH at frequencies above 0.1 Hz and that instruments coupled to the mock-lander perform comparably to ground-based instrumentation in the frequency band of 0.1–10 Hz. For future icy ocean world missions, a deck-coupled seismometer would perform similarly to a ground-based deployment across the most frequency bands.

An enhanced artificial neural network model using the Harris Hawks optimiser for predicting food liking in the presence of background noise

This paper presents an enhanced artificial neural network (ANN) model for predicting the liking of food using the Harris Hawks optimiser (HHO) in the presence of different masking background noise types and levels, relative to the ambient background noise (i.e. no noise conditions). The results showed that the proposed model can predict the relative liking food ratings with higher performance (R2 = 0.70, RMSE = 0.8), as compared to traditional ANNs using feedforward neural networks (FFNNs) (R2 = 0.61, RMSE = 1.1) and statistical mixed models (R2 = 0.42, RMSE = 1.8). This model was used to find the threshold level that gives maximum relative food liking ratings for different types of noise. This threshold level varied between 30 and 35 dBA for three noise types. The liking of food in the presence of background noise depends on acoustic and non-acoustic factors. A feature analysis using the “RReliefF” algorithm was used to investigate the relative importance of these acoustic and non-acoustic factors on the relative food liking using predicted model outcomes. Acoustic factors such as noise type and level had higher importance weights on the relative liking of food in the presence of background noise than non-acoustic factors such as gender, sensitivity, and age. The results presented herein are relevant for future and more targeted noise assessment and mitigation strategies in dining areas.

Passive Shallow Water Automated Target Recognition using Deep Convolutional Bi directional Long Short Term Memory

The extremely challenging nature of passive acoustic surveillance makes it a key area of research in NavalNon-Co-operative Target Recognition especially in Anti-Submarine Warfare systems. In shallow waters, thecomplex acoustics due to the highly varying ambient background noise as well as the multi-modal propagation in the surface-bottom bounded channel makes surveillance even difficult. In this work, an ensemble of Convolutional Neural Networks and Bidirectional Long Short Term Memory stages employing soft attention is used to effectively capture the spectro-temporal dynamics of the target signature. In order to alleviate the overall computational cost associated with the optimal model search in the extensive hyperparameter space, a recursive model elimination scheme, making frugal use of the available resources, is also proposed. Experimental analysis on acoustic target records, collected from the shallows of Arabian Sea, has yielded encouraging results in terms of model accuracy, precision and recall.

The effect of age, gender and noise sensitivity on the liking of food in the presence of background noise

The liking of food in the presence of background noise has been associated with the noise type and level. So far, however, there have been few studies investigating the non-acoustic factors associated with food perception in the presence of background noise. This study investigated the food liking due to three non-acoustic factors (i.e. gender, noise sensitivity and age) in the presence of background noise, relative to the ambient background noise (i.e. no noise conditions). Fifteen participants rated the liking of food via questionnaires. The perceptual relative food liking due to age, gender and noise sensitivity at different noise types and levels were presented. The results indicated that age, noise sensitivity and gender influence relative food liking. Females had lower liking ratings of food than males (p = 0.038). Noise sensitivity was also negatively correlated with the relative liking of food (r = −0.72, p < 0.001). Sensitive participants gave lower relative food liking ratings (p = 0.023). The older participants also gave lower relative food liking ratings (p = 0.01). A better understanding of acoustic and non-acoustic factor effects on food perception can be an important area of interest in noise management of dining areas. These results also provide an opportunity for future practical and educational applications. These include a better service that could be presented from food providers and more practical acoustic design of dining areas to suit different groups of people.

How DIFAR sensors can enhance detection and 2-D localization of impulsive fish sounds on coral reefs

The impulsive sounds produced by tropical fish are a prominent component of coral reef acoustic environments off Hawaii. The resultant ambient noise field is highly nonstationary, making it difficult to equalize the noise background when implementing standard intensity-based detectors on conventional hydrophones. Here we demonstrate how DIFAR sensors can be used to enhance the contrast between transient fish signals and background ambient noise, permitting simultaneous detection and triangulation of individual pulses. This approach assigns an azimuth to each time-frequency component of a conventional spectrogram, by computing the arctangent of the active intensity measured on two orthogonal axes. The resulting “azigram” can be processed using standard image processing methods to isolate connected regions that share the same azimuth, and to match similar regions on azigrams from nearby DIFAR sensors. The cross-matched bearings can then be used to triangulate the source. The technique is being used to study “hotspots” of fish activity on coral reef pinnacles.

Eavesdropping grey squirrels infer safety from bird chatter

When multiple species are vulnerable to a common set of predators, it is advantageous for individuals to recognize information about the environment provided by other species. Eastern gray squirrels (Sciurus carolinensis) and other small mammals have been shown to exploit heterospecific alarm calls as indicators of danger. However, many species–especially birds—emit non-alarm auditory cues such as contact calls when perceived predator threat is low, and such public information may serve as cues of safety to eavesdroppers. We tested the hypothesis that eavesdropping gray squirrels respond to “bird chatter” (contact calls emitted by multiple individuals when not under threat of predation) as a measure of safety. We compared vigilance behavior of free-ranging squirrels in the presence of playbacks of bird chatter vs non-masking ambient background noise lacking chatter after priming them with a playback recording of a red-tailed hawk (Buteo jamaicensis) call. Squirrels responded to the hawk call playbacks by significantly increasing the proportion of time they spent engaged in vigilance behaviors and the number of times they looked up during otherwise non-vigilance behaviors, indicating that they perceived elevated predation threat prior to the playbacks of chatter or ambient noise. Following the hawk playback, squirrels exposed to the chatter treatment engaged in significantly lower levels of vigilance behavior (i.e., standing, freezing, fleeing, looking up) and the decay in vigilance behaviors was more rapid than in squirrels exposed to the ambient noise treatment, suggesting squirrels use information contained in bird chatter as a cue of safety. These findings suggest that eastern gray squirrels eavesdrop on non-alarm auditory cues as indicators of safety and adjust their vigilance level in accordance with the vigilance level of other species that share the same predators.

Balance and Sound Conditions in Adults with Bilateral Cochlear Implants

Purpose: To determine if (1) balance is impaired in patients with bilateral cochlear implants compared to healthy controls and (2) the presence of sound, non-speech, or speech affects standing balance. Materials and Methods: Four patients with bilateral cochlear implants were tested on three balance conditions on Romberg tests on medium-density compliant foam with eyes closed, with head stationary or moving in yaw or pitch, under 5 sound conditions: no sound, ambient background noise, pink noise, foreign language, English language. Results: Dependent measures were duration of standing and kinematics. Three of four subjects performed well with head still and no sound, background noise, or pink noise. All subjects performed poorly during the head movement conditions when hearing either foreign-language or English words. Subjects could not perform enough head movements during yaw and pitch conditions for accurate kinematic measurements. Conclusion: The no-sound condition did not influence standing balance skills. The addition of ambient or pink noise also did not affect their balance. However, when subjects were distracted by paying attention to words, regardless whether or not they understood the words, standing balance skills deteriorated. Thus, distracted attention in these patients leads to impaired balance, which may impair functional motor skills.

Ambient noise decreases detectability of songbird vocalizations in passive acoustic recordings in a consistent pattern across species, frequency, and analysis method

ABSTRACTThe presence of ambient noise is a potential constraint that can impact studies that rely on passive acoustic recording units to compare biotic signals across a range of ambient noise levels. To investigate the hypothesis that ambient noise will impact the detectability of recorded acoustic signals, we projected noise with different amplitudes and frequency distributions into one microphone on a passive acoustic recording unit, while recording simultaneously from a second unit free of noise stimuli. We compared detection rates for five frequently recorded bird species derived from three different analysis methods. As predicted, amplitude had a strongly negative impact on detectability. There were also absolute differences in detection rates between species and the analysis methods, but the pattern of change as amplitude increased did not differ across treatments. The frequency distribution of the noise stimuli had no impact on detectability. Based on the consistent impact of amplitude on signal detection, we formulated a single correction factor to estimate the proportion of detections being lost to background ambient noise. While this formula will benefit from additional testing across an array of species and conditions, it is clear that ambient noise alters detection rates in passive acoustic recordings.

Deep learning for underwater noise classification

Deep-learning models have surpassed many computer-vision benchmarks and groups such as Google have begun to investigate similar methods for understanding underwater data. Understanding underwater soundscapes is critical to many applications such as assessing the impacts of anthropogenic noise on sea life and monitoring the health and biodiversity of the ocean. In this work, we present a computer-vision approach for classifying audio signals to distinguish whale sounds—especially, vocalizations from mysticetes—from other sources of sound in the underwater soundscape, such as ships. This is a challenging problem due to wide variation in ambient background noise, sensor configuration and properties, and whale vocalization patterns within and across species. Here, we adapt deep convolutional neural networks (CNN) to analyze spectral patterns of common noise sources and demonstrate robust performance on a dataset of ambient noise derived from multiple open-source databases including whale vocalizations from eight species and shipping noise from over ten platforms observed across multiple environments with a variety of sensors. Performance of the network is characterized in terms of classification accuracy and generalizability as a function of CNN hyperparameters and training architecture. With a CNN trained from scratch, we analyze the learned features of the classification decisions of the network by adapting several visualization techniques from the computer-vision domain.

Parallel evolutionary forces influence the evolution of male and female songs in a tropical songbird.

Given the important role that animal vocalizations play in mate attraction and resource defence, acoustic signals are expected to play a significant role in speciation. Most studies, however, have focused on the acoustic traits of male animals living in the temperate zone. In contrast to temperate environments, in the tropics, it is commonplace for both sexes to produce complex acoustic signals. Therefore, tropical birds offer the opportunity to compare the sexes and provide a more comprehensive understanding of the evolution of animal signals. In this study, we quantified patterns of acoustic variation in Rufous-and-white Wrens (Thryophilus rufalbus) from five populations in Central America. We quantified similarities and differences between male and female songs by comparing the role that acoustic adaptation, cultural isolation and neutral genetic divergence have played in shaping acoustic divergence. We found that males and females showed considerable acoustic variation across populations, although females exhibited greater population divergence than males. Redundancy analysis and partial-redundancy analysis revealed significant relationships between acoustic variation and ecological variables, genetic distance, and geographic distance. Both ambient background noise and geographic distance explained a high proportion of variance for both males and females, suggesting that both acoustic adaptation and cultural isolation influence song. Overall, our results indicate that parallel evolutionary forces act on male and female acoustic signals and highlight the important role that cultural drift and selection play in the evolution of both male and female songs.

Sounds like a healthy retail atmospheric strategy: Effects of ambient music and background noise on food sales

Retail atmospherics is becoming an increasingly important strategic tool for stores and restaurants. Ambient music and background noise are especially important atmospheric elements given their ubi ...

Expected Seismicity and the Seismic Noise Environment of Europa

AbstractSeismic data will be a vital geophysical constraint on internal structure of Europa if we land instruments on the surface. Quantifying expected seismic activity on Europa both in terms of large, recognizable signals and ambient background noise is important for understanding dynamics of the moon, as well as interpretation of potential future data. Seismic energy sources will likely include cracking in the ice shell and turbulent motion in the oceans. We define a range of models of seismic activity in Europa's ice shell by assuming each model follows a Gutenberg‐Richter relationship with varying parameters. A range of cumulative seismic moment release between 1016 and 1018 Nm/yr is defined by scaling tidal dissipation energy to tectonic events on the Earth's moon. Random catalogs are generated and used to create synthetic continuous noise records through numerical wave propagation in thermodynamically self‐consistent models of the interior structure of Europa. Spectral characteristics of the noise are calculated by determining probabilistic power spectral densities of the synthetic records. While the range of seismicity models predicts noise levels that vary by 80 dB, we show that most noise estimates are below the self‐noise floor of high‐frequency geophones but may be recorded by more sensitive instruments. The largest expected signals exceed background noise by ∼50 dB. Noise records may allow for constraints on interior structure through autocorrelation. Models of seismic noise generated by pressure variations at the base of the ice shell due to turbulent motions in the subsurface ocean may also generate observable seismic noise.

Entrainment of Voluntary Movement to Undetected Auditory Regularities

In physics “entrainment” refers to the synchronization of two coupled oscillators with similar fundamental frequencies. In behavioral science, entrainment refers to the tendency of humans to synchronize their movements with rhythmic stimuli. Here, we asked whether human subjects performing a tapping task would entrain their tapping to an undetected auditory rhythm surreptitiously introduced in the guise of ambient background noise in the room. Subjects performed two different tasks, one in which they tapped their finger at a steady rate of their own choosing and one in which they performed a single abrupt finger tap on each trial after a delay of their own choosing. In both cases we found that subjects tended to tap in phase with the inducing modulation, with some variability in the preferred phase across subjects, consistent with prior research. In the repetitive tapping task, if the frequency of the inducing stimulus was far from the subject’s own self-paced frequency, then entrainment was abolished, consistent with the properties of entrainment in physics. Thus, undetected ambient noise can influence self-generated movements. This suggests that uncued decisions to act are never completely endogenous, but are subject to subtle unnoticed influences from the sensory environment.