Noise Events Research Articles

Animal-borne sensors reveal high human impact on soundscapes near a critical sea turtle nesting beach

Anthropogenic noise (anthrophony) is pervasive in natural soundscapes and has become an important aspect of conservation. While moored sound recorders have aided marine soundscape research, they do not capture the dynamic experiences of animals as they move through underwater soundscapes.This study used animal-borne acoustic recording tags to capture the marine soundscape near leatherback turtle nesting grounds in Gabon, Central Africa. Propeller noise was heard in 75 ± 14.7 % (mean ± SD) of recordings and peaks in sound intensity up to 146 dB re 1 μPa were detected in shipping noise frequency bands. Loud noise events (> 141 dB), detected in 10 % of recordings, were distributed throughout the turtle interesting habitat. An anthrophony map was created, identifying peaks in noise corresponding with the Komo estuary —gateway to the nation's main international port and near a key nesting area. The pervasive, loud anthrophony recorded in the study may have negative impacts on nesting leatherback turtles and other species of conservation concern found in the area, and warrants further monitoring and management action.This study offers one of the first spatio-temporal analyses of sound experienced by an endangered marine vertebrate through animal borne, multi-individual acoustic monitoring. It highlights the utility of animal borne acoustic tags in delineating underwater soundscapes and their applicability to studying concurrent biological phenomena and threats, while supporting the need for similar monitoring efforts in other critical sea turtle habitats.

Unattended noise measurements: use of new technologies to automatically qualify noise events for greater efficiency, precision and time savings

For more than 40 years, the noise assessment in the environment has consisted of measuring and storing the time history typically every second, then marking the events using post-processing software or automatically or manually by coding. Technological advances now make it possible to enrich the simple measurement of the time history thanks to additional data allowing a better evaluation of the measured phenomena: audio recording and AI analysis of the type of noise, analysis of the predominant direction during a noise event. We propose to present an innovative solution based on a 3D sound intensity probe allowing the determination of the azimuth and the elevation every time a noise event occurs. The results are expressed by frequency bands and A weighted filter. The influence of the type of field (active and reactive intensity) will be developed. Particular attention will be paid to the accuracy and the validity of the measurement as well as the limits of the principle.

A sound recognition algorithm to filter non construction site noise

Sixense Engineering have developed a new algorithm to correct the noise levels measured around construction sites, by identifying and filtering out sounds that are generated by activities outside the site (emergency services sirens, traffic horns, etc.). The idea is to go beyond the simple classification of audio files by applying the classification algorithm to all the noise events that contribute to the noise dose LAeq,T. In this innovative process, the noise dose is calculated from elementary LAeq,1min,. When noise events are detected (in terms of LAeq,1min), the corresponding audio recording (.wav file) are analysed by the sound recognition algorithm. When the algorithm identifies that the noise source is not construction related, with a high degree of confidence, the corresponding LAeq,1min is excluded from the noise dose calculation. In other words, the measured noise dose can be filtered by removing non construction related noise events produced by external sources like sirens, horns, motorbikes, birds, music, etc.

On the use of the intermittency ratio for the assessment of aircraft noise during the night

This paper explores the possibility of using the intermittency ratio as a novel metric for evaluating aircraft noise during nighttime periods. Using Dublin airport as a test case, the paper presents an analysis of nighttime flight patterns and corresponding noise data. It outlines the limitations of using a simple Lnight indicator to capture the temporal variation of noise. Similar limitations are observed with the use of a Noise Quota System. It is proposed that such assessments should be complemented with additional indicators that consider how many noise events (and noise induced awakenings) occur throughout the nighttime period. One indicator that has been suggested may be appropriate for use is the 'Intermittency Ratio', and as such this study investigates how the intermittency ratio might capture the temporal variations in noise emissions around Dublin Airport. Results suggest that while the metric offers some additional insight into the acoustic climate in the environs of an airport, it is not (yet) suitable for distinguishing between scenarios that can yield similar Lnight values, but significantly different temporal changes. The findings underscore the need to use additional indicators in the assessment of nighttime noise from aircraft.

Application of noise remote sensing to conquer loud vehicles in Amsterdam

Techniques for remote sensing of noise are rapidly evolving, allowing for automatic and unsupervised monitoring of individual noise events. In the H2020 project NEMO, noise remote sensing was developed and applied to detect individual loud vehicles in normal road or rail traffic. Potential applications include urban vehicle access restrictions (UVAR) such as in low emission zones, based on real world emissions rather than type approval data. The city of Amsterdam is also experimenting with influencing driving behaviour and raising road user awareness to reduce the number of high noise emitters. Amsterdam uses dynamic roadside signalling for this, which shows a message "too loud" to noise vehicles passing by. New tests are now performed with a more advanced acoustic camera to localize and track the individual vehicle, connected to license plate and speed detectors. This paper describes how NEMO results are used to achieve an actual reduction of high emitters in Amsterdam. First results of experiments are presented along with technical challenges that need to be overcome. These challenges include reduction of measurement uncertainty as well as uncertainty as to what vehicle a high noise event belongs to.

A comparative study of noise event identification using AI in unattended monitoring

This paper explores the use of multi-microphone devices and artificial intelligence (AI) for identifying noise events in unattended noise monitoring. The primary focus is to assess the reliability of a machine learning model initially trained on a dataset representing a particular soundscape. We evaluate the performance of this model when applied to diverse datasets collected from similar yet distinct soundscapes, encompassing various environmental conditions and noise profiles. Through comparative analysis, we determine the model's adaptability and potential limitations. The findings of this study offer insights into how well AI-based noise event identification models can work in different situations. This lays the groundwork for enhancing their applicability in diverse real-world settings and improving how well unattended noise monitoring systems function.

Participative pilot study at the vicinity of airports for the construction of a noise point counter integrating instantaneous annoyance at overflight

Current regulations on transport noise are based on energetic acoustic indicators that do not adequately reflect the intensity and repetitive nature of noise peaks, particularly for aircraft overflights or rail traffic. The public debate on these issues therefore often centres on the questioning of these indicators, which are deemed to be inadequate to reflect the annoyance caused to local populations, particularly in a context of increasing traffic. Bruitparif suggests exploring a noise events counter, the Noise Point Counter (NPC), based on existing NAX indicators but freeing itself from the threshold effect, which is a drawback to their use. The idea is to count the number of noise events, weighting each event according to the level of instantaneous annoyance it is likely to generate for local residents. To make progress on the feasibility of developing such an indicator and to confirm its relevance, Bruitparif is proposing to carry out a pilot study on three selected overflown areas in the Île-de-France region, involving around thirty local residents on each occasion. The paper will present the principle behind the creation of this new indicator as well as the pilot study protocol.

Aircraft automatic detection on Station7hz interface using signal processing and artificial intelligence - ACOUDET - First method

The global project is to design a automatic noise event detection and classification system in real time using signal processing and artificial intelligence (AI). The first phase focuses on transport noise (aircrafts, trains, road vehicles, etc.). Secondly, the plan is to extend detection to construction site noise and any natural or life sources. This paper presents the first method developed to detect audible aircrafts pass-by. It focused on the development of a prototype detection algorithm, its results in situ conditions, and insights on future developments to create a robust aircraft and train noise detection system. The data collection method, aircraft audio signal particularities and a first detection method are described. The robustness of the detections is evaluated and limitation induced by real time detection are discussed. Thanks to the proprietary monitoring system Station-7hz. it has been possible to detect aircrafts in different types of sound environments in a straightforward manner, showing better results at night due to a quieter environment. Future developments based on a hybrid algorithm include AI models for audibility assessment and unwanted event filtering during aircraft pass-by. Aircraft's presence is detected via radar data and the detection of maximum acoustic levels correlated with aircraft parameters.

Experimental assessment of isolation performance of a building above metro lines: a case study with dual isolation levels

This paper presents an assessment of the acoustic performance of a five-story residential building constructed directly above a metro line in Prague. The building has been isolated at two levels: first, on top of the distribution beams above the metro tunnel and secondly, at the ground floor. The assessment of the building's performance has been conducted by a direct measurement of ground borne noise radiated inside the rooms and evaluating the vibration transmission loss through the isolation system. The transmission loss assessment was carried out using two different test setups. Firstly, during train pass-byes, ground borne vibrations were simultaneously measured at various locations above and below the vibration cut. Secondly, at the same measurement points, the vibration levels were measured using hammer impacts. Finally, the transmissibility obtained from both test setups was compared and discussed. Additionally, ground borne noise levels were measured inside rooms on different floors above the vibration cut. To identify noise events exclusively associated with train pass-byes, data obtained from synchronized vibration measurements at the center of a room inside an apartment were used. Finally, the measured noise levels were compared with the limit values of noise exposure recommended by the Czech standards.

Influence of the number and sound level of noise events and task engagement on perceived loudness and annoyance

It is not clear how the distribution of noise sound-energy over time influences people. We compared perceived loudness, annoyance and ability to perform a cognitive task in people exposed to recordings of aircraft noise, presented either as a single 15-second overflight at 80 dB LeqA15seconds or four 15-second overflights at 60 dB LeqA15seconds. Levels were chosen on the basis that an increase of 10 dB doubles the perceived loudness, thus four stimuli at 60 dB might be expected to seem as loud as a single stimulus at 80 dB. Participants performed an engaging mental arithmetic task during half the noise presentations and rated their perception of loudness and annoyance to every presentation. Overall, the single 80-dB-flight stimulus was perceived as louder and more annoying than the four 60-dB flights. While doing the task, the four 60-dB flights were perceived as louder and more annoying than without the task, whereas the single 80-dB stimulus was less loud and no more annoying than without the task. Noise did not influence task performance. Future research may explore systematically the relationship between the number and sound level of overflights and the reactions they induce.

Improving knowledge of the acoustic factors involved in railway noise annoyance: first results of a pilot field survey of sixty-two local residents

Railway transportation contributes to the objectives of decarbonization but also generates negative externalities, including noise. Energy noise indicators used to characterize population exposure do not adequately reflect the repetitive nature of railway noise peaks. The GENIFER pilot study aims to test a protocol designed to characterize railway noise events according to the instantaneous perceived annoyance when the train is passing, in order to improve understanding of the influence of acoustic factors on annoyance. The first phase of the survey was carried out in 2023 among 62 residents of a pilot site. An electronic device was used to collect around 5,000 ratings, ranging from 1 to 10, assessing the instantaneous annoyance induced by train noise at passing trains. The site instrumentation included sixteen sound level meters and two video recording systems, enabling annoyance ratings to be associated with the acoustic characteristics of railway noise events. A questionnaire aimed at identifying co-determinants of long-term annoyance was also administered to participants. Feedback on the field implementation of this survey and initial results concerning acoustic measurements, instantaneous annoyance ratings and questionnaire responses will be presented.

Optimized search for a binary black hole merger population in LIGO-Virgo O3 data

Maximizing the number of detections in matched filter searches for compact binary coalescence (CBC) gravitational wave signals requires a model of the source population distribution. In previous searches using the framework, sensitivity to the population of binary black hole (BBH) mergers was improved by restricting the range of filter template mass ratios and use of a simple one-dimensional population model. However, this approach does not make use of our full knowledge of the population and cannot be extended to a full parameter space search. Here, we introduce a new ranking method, based on kernel density estimation with adaptive bandwidth, to accurately model the probability distributions of binary source parameters over a template bank, both for signals and for noise events. We demonstrate this ranking method by conducting a search over LIGO-Virgo O3 data for BBHs with unrestricted mass ratio, using a signal model derived from previous significant detected events. We achieve over 10% increase in sensitive volume for a simple power-law simulated signal population, compared to the previous BBH search. Correspondingly, with the new ranking, eight additional candidate events above an inverse false alarm rate threshold 0.5 yr are identified. Published by the American Physical Society 2024

An improved pixel circuit with low noise event rate and enhanced bright‐light sensitivity for dynamic vision sensor

AbstractDynamic vision sensor (DVS) imaging quality is significantly affected by pixel noise and temporal contrast (TC), which is inversely proportional to sensitivity. To reduce the noise event rate and improve sensitivity in bright‐light conditions in the DVS pixel circuit, this paper proposes improvements to the conventional DVS pixel circuit. The proposed DVS pixel circuit adopts stacked medium‐threshold transistors instead of a single high‐threshold transistor in the photoreceptor and introduces a threshold switching circuit. Compared with the conventional DVS pixel circuit, this design increases event threshold normalized by root mean square (RMS) noise voltage, reducing the dim‐light noise bandwidth. Additionally, it achieves higher sensitivity in bright‐light conditions compared with dim‐light conditions. The proposed DVS pixel circuit is implemented in a 110‐nm complementary metal‐oxide semiconductor (CMOS) process. Post‐simulation results show that, for photocurrents between 5 fA and 100 pA, the proposed DVS pixel circuit achieves a 35 Hz peak event rate at 15% TC, which is reduced to 3.1% of the conventional structure. For photocurrents exceeding 30 pA, the proposed structure can switch TC from 15% to 5%, maintaining a noise event rate below 0.1 Hz.

Integrating multi-microphone devices and AI for noise event identification in unattended monitoring

This paper explores the use of multi-microphone devices and artificial intelligence (AI) for the identification of noise events in unattended noise monitoring. The system presented aids in the identification and localization of dominant sound sources using time difference of arrival techniques. By incorporating AI technology, the system's software automates the extraction of non-relevant sounds. The algorithms enable removal of undesired components, resulting in a more accurate measurement. This article discusses the benefits and limitations associated with employing automatic processes in noise monitoring and analysis software. Finally, a current application and prospects for future advancements in the field are presented.

PARTNER project 1, low-frequency noise, and FAA's noise policy review

To improve upon current FAA noise policy, it seems reasonable to consider supplemental metrics. One of these is to include the annoyance impacts of low-frequency noise (LFN). It is established that LFN can cause rattle in homes near airports, causing annoyance. Current DNL 65 does not address LFN. In fact, most acoustical measurements do not include low frequencies due to the limitations of inexpensive measurement equipment. Hence, the FAA is genuinely missing these impacts from a physics point of view. The FAA has already paid for a very high quality LFN study through Project 1 in the PARTNER Center of Excellence (predecessor of the current ASCENT center). That project completed in 2007. The present author was not a participant in PARTNER Project 1, but he closely observed the amazing work done by the investigators from The Pennsylvania State University, Purdue University, and the University of Central Florida. If sufficient LFN data is regularly obtained at larger airports with improved noise monitoring equipment, then FAA could include supplemental metrics, such as the number of low-frequency noise events in a given period. [Work supported by the Penn State College of Engineering and its United Technologies Corporation Professorship.]

Sequencing-induced artefacts in NGS STR data

Significant progress has been made in recent years in the development of techniques for Next Generation Sequencing (NGS), or Massively Parallel Sequencing (MPS), of forensically relevant short tandem repeat (STR) loci. However, as these technologies are investigated and adopted by forensic laboratories, new challenges unfold that require further scrutiny. In the analysis of DNA profiles generated using the MiSeq FGx sequencing system, we have observed noise sequences with relatively high readcounts that are challenging to distinguish from genuine alleles. These high read count noise sequences appear as allele sequences with one or a few substituted bases compared to a known allele sequence within the profile.An examination of ForenSeq DNA Signature Prep Kit STR noise sequences revealed that the substituted base of a parent allele can align to the same position on the sequence across noise sequences. This suggests that these substitution events occur at specific positions within the amplicon, resulting in multiple noise reads with substitutions at the same position. Mapping of the noise events onto the original raw read positions revealed a high number of events, or “noise spikes”, occurring atspecific positions within a given sequencing run. These noise spikes affected reads across the entire run, agnostic of locus or sample, while the position, occurrence, and amplitude of the spikes differed across runs. The majority of noise sequences with high read counts in a DNA profile were generated from base changes at these spike positions, and could be classified as “noise spike artefacts”.In this paper we present evidence of the noise spike artefacts and their genesis during the sequencing process in the sequencing-by-synthesis (SBS) cycles, as well as the methods developed to detect them. The information and methods will assist laboratories with detecting noise spikes in MiSeq FGx sequencing runs, differentiating authentic allele sequences from noise spike artefacts, and developing protocols for analyst review and handling of MiSeq FGx data.

A long-term global and clustering analysis of the COVID-19 pandemic effects on the sound environment of Milan, Italy: Road traffic and other noise sources

The different measures taken by the competent authorities to tackle the effects of the COVID-19 pandemic affected the acoustic environment worldwide, resulting in significant reductions of noise levels. However, most of the investigations mainly focused on the lockdown during spring 2020, in which all non-essential industrial and commercial activities were prohibited, being people requested to stay at home. The analysis of the data collected and processed by the wireless acoustic sensor network deployed in Milan, Italy, by the DYNAMAP project showed a significant drop of sound levels, mainly due to road traffic as well as other noise sources, denoted as anomalous noise events (ANEs). This work extends that research through a long-term global analysis of the COVID-19 pandemic effects from January 2020 to August 2021, including data clustering to identify local patterns of the sound environment of Milan. As a result of the first hard and subsequent second soft lockdown imposed by the Italian authorities, a double V-shape reduction pattern of the mean sound levels Lden (with and without ANEs) is observed, with maximum drops of around 6 dB and 3 dB, respectively, surrounded with similar progressive reductions in both pre- and post-lockdown periods, showing statistically significant differences. A complementary pattern is obtained regarding the detection of ANEs. The clustering of Lden indicators yields three different sound patterns in the second lockdown, both in terms of noise levels and ANEs, depending on sensor locations and traffic flow variations, unlike the first lockdown with a homogeneous behaviour throughout the city.

Artificial intelligence to detect noise events in remote monitoring data

AbstractBackgroundRemote monitoring (RM) of cardiac implantable electrical devices (CIEDs) can detect various events early. However, the diagnostic ability of CIEDs has not been sufficient, especially for lead failure. The first notification of lead failure was almost noise events, which were detected as arrhythmia by the CIED. A human must analyze the intracardiac electrogram to accurately detect lead failure. However, the number of arrhythmic events is too large for human analysis. Artificial intelligence (AI) seems to be helpful in the early and accurate detection of lead failure before human analysis.ObjectiveTo test whether a neural network can be trained to precisely identify noise events in the intracardiac electrogram of RM data.MethodsWe analyzed 21 918 RM data consisting of 12 925 and 1884 Medtronic and Boston Scientific data, respectively. Among these, 153 and 52 Medtronic and Boston Scientific data, respectively, were diagnosed as noise events by human analysis. In Medtronic, 306 events, including 153 noise events and randomly selected 153 out of 12 692 nonnoise events, were analyzed in a five‐fold cross‐validation with a convolutional neural network. The Boston Scientific data were analyzed similarly.ResultsThe precision rate, recall rate, F1 score, accuracy rate, and the area under the curve were 85.8 ± 4.0%, 91.6 ± 6.7%, 88.4 ± 2.0%, 88.0 ± 2.0%, and 0.958 ± 0.021 in Medtronic and 88.4 ± 12.8%, 81.0 ± 9.3%, 84.1 ± 8.3%, 84.2 ± 8.3% and 0.928 ± 0.041 in Boston Scientific. Five‐fold cross‐validation with a weighted loss function could increase the recall rate.ConclusionsAI can accurately detect noise events. AI analysis may be helpful for detecting lead failure events early and accurately.

Development of a compact multiprobe system for monitoring positron-emitting tracers in plant stems

Nondestructive monitoring of positron-emitting tracers in plant bodies at multiple points, including points separated by large distances, has been realised with the positron multiprobe system (PMPS) to investigate graminaceous plants, whose stems and leaves have simple shapes. Recently, the translocation of photosynthates into fruits has been studied intensively using a 11C tracer. The relatively complex shapes of the stems and leaves of these plants sometimes prevent the detector heads of the PMPS from approaching and being fixed to the target stem properly because of its relatively large and heavy detector heads based on photomultiplier tubes. Owing to the compactness, lightweightness and recent advances of silicon photomultipliers (SiPMs), fabricating compact and lightweight detector heads has become possible. In this study, we developed a compact PMPS (CPMPS) using SiPMs and successfully demonstrated its capability for monitoring a 11C tracer in strawberry stems. Moreover, we found that energy-window filtering markedly reduced noise events without radiation shielding. The dominant ionisation events detected by the CPMPS were Compton scattering and subsequent photoabsorption of a single 511 keV gamma ray, suggesting that the ionisation events of single-gamma-ray emitters, such as 42K, 43K, 54Mn, 59Fe and 65Zn, can be detected by the CPMPS. The developed CPMPS can also be applied to study the physiology of other plants with intricately shaped stems and leaves, such as the tomato and eggplant.

Sensing microscopic noise events by frequent quantum measurements

Sensing microscopic noise events by frequent quantum measurements