Noise monitoring system

Environmental noise monitoring systems continuously measure sound levels without assigning these measurements to different noise sources in the acoustic scenes, therefore incapable of identifying the main noise source. In this paper a feasibility study is presented on a new monitoring concept in which an acoustic pattern classification algorithm running in a wireless sensor is used to automatically assign the measured sound level to different noise sources. A supervised noise source classifier is learned from a small amount of manually annotated recordings and the learned classifier is used to automatically detect the activity of target noise source in the presence of interfering noise sources. The sensor is based on an inexpensive credit-card-sized single-board computer with a microphone and associated electronics and wireless connectivity. The measurement results and the noise source information are transferred from the sensors scattered around the measurement site to a cloud service and a noise portal is used to visualise the measurements to users. The proposed noise monitoring concept was piloted on a rock crushing site. The system ran reliably over 50days on site, during which it was able to recognise more than 90% of the noise sources correctly. The pilot study shows that the proposed noise monitoring system can reduce the amount of required human validation of the sound level measurements when the target noise source is clearly defined.

An apparatus adapted to be worn in an environment in which unsafe noise levels may be present for the purpose of continuously monitoring the noise level impinging upon the ear(s) of a user. The noise levels are monitored via a microphone housed within a hearing protective device, and located such that the noise level measured by the microphone is representative of the noise level impinging upon the ear(s) of the user when the hearing protective device is being worn in either a primary or secondary position. The noise level is recorded along with its duration to calculate a cumulative noise dose for an individual user, and to warn the user of when the noise dose exceeds a preset level. The apparatus further provides for a means of transmitting the noise level and dose information to a data storage/retrieval device such as a PC for maintaining a history of noise exposure for the individual user of the device. A method of using an apparatus as described, or a similar device, to continuously monitor an individual's noise exposure is also disclosed.

This paper presents the use of noise monitoring system, terminal NMT 3637B and software 7802, Brüel & Kjær in process of jet aircraft noise certification according to standard ICAO Annex 16 and the algoritam for calculating EPNL noise level (Effective Perceived Noise Level) that we created in MatLab environment. This software sublimated all necessary input parameters, acoustic and non acoustic in purpose of getting final result and intermediate data. Those data are important for reporting to the certificating authority.

Airport noise has been monitored at Logan International Airport since 1975. Recently, Massport decided to install a new noise monitoring system. The new system will monitor trends in noise impact by time of day, season, and on an annual basis; measure noise levels generated by aircraft taxiing, ground operations, reverse thrust, takeoffs, landings, and overflights; obtain accurate data on aircraft flight tracks and fleet mix to be used in the generation of noise contours; and assess the performance of the Logan Preferential Runway Advisory System in terms of runway utilization and noise exposure. To do this, noise data from some sites will be obtained in 1/3-octave bands as well as in the traditional A-weighting; C-weighting is available; radar tracking information is expected to be part of the system; and weather data will be obtained at about half of the sites. All data are saved in database for study of the correlation of noise with various factors. A complaint database, contour prediction mode (FAA integrated noise model), and mapping software are also part of the system. It is expected that this system will be used to evaluate different noise descriptors against actual complaints, and to suggest improvements to noise prediction models based on geography or meteorological effects.

This paper presents the research of noise level monitoring at the Zagreb Airport Ltd. The purpose of this paper is to show how the aircraft noise sources can be used as a tool for aircraft detection and for increasing flight safety. The noise measurement was made at the Zagreb Airport by using its professional noise monitoring system. The research has led to new findings, such as determining the aircraft model by measuring the noise level generated by an aircraft during final approach based on its frequency spectrum, as the connectivity from the airport’s side to the radar data was unavailable (only ATC). In addition, it is possible to determine the aircraft altitude and, perhaps most significantly, the increase of flight safety through the detection of potential failures on the aircraft structure and/or engine during the overflight of a noise monitoring terminal.

Present work was carried out to collect primary data from the field with the help of noise meter and thus spatial anddiurnal variation of background and aircraft noise level was analyzed. Seventeen sampling sites were chosennearby Tribhuwan International Airport (TIA) and air route of aircraft flyover. Noise measurements were carried outunder normal atmospheric environmental condition at temperature ranges from 25°C-30°C excluding rainy andwindy days. Among the sampling sites, the Equivalent Continuous Noise Level (LAeq) of background noise measuredmaximum at Balkumari (73.2 dBA) and minimum at Datidol (49.2 dBA). Noise meter was allowed to record aircraftnoise level when aircraft produced noise greater than background noise level and recorded until it reduced tobackground noise level. The maximum value of aircraft noise level, (LAeq) measured at Gothatar (101.5 dBA) whichlies north east from the TIA and at the same height as the airport ground and minimum at Datidol (63.5 dBA).Toanalyse the spatial variation of aircraft noise of different sampling sites, contour map was plotted with the help oforigin software. The LAeq of more than 60 percent sampling sites fall under the severe noise exposure class (> 75dBA) which causes substantial hearing loss. Implementation of noise control measurement and public awarenessare recommended to control adverse effect of noise pollution.DOI: http://dx.doi.org/10.3126/njst.v15i1.12031Nepal Journal of Science and TechnologyVol. 15, No.1 (2014) 139-144

Objective:Orthopedic surgeries generate high noise levels that exceed recommended thresholds, thereby posing risks to surgical safety. Nurses play a critical role in instrument preparation and intraoperative monitoring. However, evidence of noise-induced cognitive impairment in this population remains limited. This study evaluated the effects of intraoperative noise exposure on nurses’ reaction times and error rates during orthopedic procedures.Methods:This retrospective study analyzed data from 200 orthopedic surgeries conducted in a tertiary hospital over 12 months. Noise levels in the operating rooms (ORs) were recorded using calibrated noise meters. Nurses’ cognitive performance was assessed by measuring reaction time and error rate. Baseline demographic and clinical data, such as age, experience, gender, and surgery type, were included. Correlation analysis and multivariate regression were performed to assess the relationships among noise levels, reaction time, and error rates.Results:The average noise level in the ORs was 89.73 ± 11.52 dB (range: 60–120 dB). Noise levels showed significant positive correlations with reaction delays (r = 0.54, P < 0.01) and error rates (r = 0.68, P < 0.01). Nurses exposed to noise levels exceeding 100 dB had an average reaction time of 274.82 ± 45.80 ms, compared with 261.48 ± 47.63 ms in quieter environments. Error rates increased sharply beyond 100 dB, with a mean of 0.91% ± 0.05%, compared with 0.61% ± 0.04% at lower noise levels. Multivariate logistic regression analysis confirmed noise as an independent risk factor for delayed reaction time and increased error rate.Conclusion:Noise exposure during orthopedic surgeries significantly affects nurses’ information processing, increasing reaction times and error rates. Noise mitigation strategies are essential to enhance OR safety and nursing performance.

In this paper the composition, operation, and performance capabilities of a shipboard noise and vibration monitoring system will be described. The uniqueness of this system is that it consists of readily available off-the-shelf instrumentation designed to measure or monitor both the mechanical and/or acoustic condition of shipboard machinery. For this particular application the system has been tailored for use aboard a new class of Navy ships.

Airport take-off noise assessment aimed at identify responsible aircraft classes

Background . Noise pollution is a burden exerting a harmful and irritating effect on human health. Children are particularly sensitive to noise burden. In this connection, it is especially important to control the noise pollution level in public education organizations where children spend significant time. Objective . The study is aimed at determining objective and subjective parameters of noise pollution in public education organizations based on the analysis of data obtained by measuring noise levels in school campuses in Saransk and directly in classrooms, as well as by questioning the teachers of these schools. Methods . The measurement of noise burden was carried out in 10 schools using the noise and vibration level meter “Assistant” with class I accuracy. Noise measurements in school campuses and classrooms were performed in accordance with the GOST 23337-2014 requirements. Assessment of results was conducted in accordance with sanitary standards 2.2.4/2.1.8.562-96 “Noise in the workplace, residential and public buildings and housing developments”. Results . Based on noise measurement results, the highest noise levels were found in school № 2 (65.5 dBА on the campus, 51.2 dBA in the classroom) which is located in the center of a traffic interchange. The lowest noise parameters were found in school № 35 (45.2 dBА on the campus, 37.7 dBА in the classroom) which is located in the residential quarter quite far away (242-321 m) from the main source of noise, the motorway. Conclusion . Study results attest that maximum noise levels at school campuses do not exceed the permissible exposure limit of 70 dBA. In all schools noise values conform to the noise standards (maximum sound pressure level in classrooms did not exceed 55 dBA).

Noise exposure is a distinct hazard during hand-held concrete grinding activities, and its assessment is challenging because of the many variables involved. Noise dosimeters were used to examine the extent of personal noise exposure while concrete grinding was performed with a variety of grinder sizes, types, accessories, and available dust control methods. Noise monitoring was conducted in an enclosed area covering 52 task-specific grinding sessions lasting from 6 to 72 minutes. Noise levels, either in minute average noise level (Lavg, dBA) or in minute peak (dBC), during concrete grinding were significantly (P < 0.01) correlated with general ventilation (GV: on, off), dust control methods (uncontrolled, wet, Shop-Vac, HEPA, HEPA-Cyclone), grinding cup wheel (blade) sizes of 4-inch (100 mm), 5-inch (125 mm) and 6-inch (150 mm), and surface orientation (horizontal, inclined). Overall, minute Lavg during grinding was 97.0 ± 3.3 (mean ± SD), ranging from 87.9 to 113. The levels of minute Lavg during uncontrolled grinding (98.9 ± 5.2) or wet-grinding (98.5 ± 2.7) were significantly higher than those during local exhaust ventilation (LEV) grinding (96.2 ± 2.8). A 6-inch grinding cup wheel generated significantly higher noise levels (98.7 ± 2.8) than 5-inch (96.3 ± 3.2) or 4-inch (95.3 ± 3.5) cup wheels. The minute peak noise levels (dBC) during grinding was 113 ± 5.2 ranging from 104 to 153. The minute peak noise levels during uncontrolled grinding (119 ± 10.2) were significantly higher than those during wet-grinding (115 ± 4.5) and LEV-grinding (112 ± 3.4). A 6-inch grinding cup wheel generated significantly higher minute peak noise levels (115 ± 5.3) than 5-inch (112 ± 4.5) or 4-inch (111 ± 5.4) cup wheels. Assuming an 8-hour work shift, the results indicated that noise exposure levels during concrete grinding in enclosed areas exceeded the recommended permissible exposure limits and workers should be protected by engineering control methods, safe work practices, and/or personal protective devices.

The Internet of Things (IoT) is an emerging paradigm that combines several technologies. IoT provides the interconnection of physical devices for exchanging information among them and provides services to the users. The urban environment is susceptible to various kinds of noise from different sources. Therefore, a system is needed to monitor the urban environment’s noise level. NoiseAware is a noise monitoring system for smart cities. NoiseAware monitors the noise levels of a particular location and sends an alert to the competent bodies to operate on site when the level reaches the allowable threshold. According to the literature review conducted, we did not find any work related to noise monitoring that has systems integration, accords real-time requests, and uses more than one communication protocol. NoiseAware has considerable service availability (approximately 81%) for system users, and the load tests show it has relevant request capability.

This paper describes the long-term noise monitoring data for ten consecutive years (2011–2020) acquired from the diversified National Ambient Noise Monitoring Network (NANMN) set up across 7 major cities of India and consisting of 70 stations for continuous noise monitoring throughout the year. The annual average ambient noise levels observed throughout these ten years in 70 locations under study, including 25 locations in commercial zones, 12 in industrial, 16 in residential, and 17 in silence zones, are described. The noise scenario in these ten years is compared and analyzed. It is observed that no site in residential or silence zones meets the ambient noise limits for the past ten years. The study suggests guidelines for a policy framework for environmental noise management and control to regulate noise pollution in Indian cities.

In the NICU, environment sounds and noise can be challenging for the preterm and/or sick newborn baby. Reducing noise to provide an environment with appropriate and meaningful auditory experiences such as parents’ voices is important. Elimination of loud noises will furthermore benefit the families and staff. In 2016, the NICU at Rigshospitalet - Copenhagen University Hospital, collaborated with SoundEar™, a company that develops noise-meters for indicating and collecting noise levels, to develop a software program that was appropriate and easy to use. The aim was to support reduction in noise levels at the NICU through different layers of nudging: The noise meters with dis-plays should help staff and families become aware of their own noise levels and change their noisy behavior. The software helps staff become aware of when and where noise levels are critical and something should be done differently. The software sends out noise reports on a weekly basis via email to key staff members, who use these reports as a basis for further discussion about noise at staff meetings.

This paper gives the results of the TRRL research programme into the noise from road construction operations. The paper deals with the measurement of noise from individual items of plant over long periods at the site boundary, with the prediction and monitoring of noise and with various methods of noise control. The noise of individual items of plant can be described in terms of a single parameter, the event energy noise level, and this parameter can be used as input to a noise prediction method. The equivalent energy continuous noise level (leq) at the site boundary can be calculated using the prediction method. The method is capable of predictions accurate to plus or minus 2db(a) when sufficient information is available as input data. Several methods of noise control are identified; efficient silencing of the plant is the most effective. In a case study on a semi-urban motorway scheme, it was found that a reduction in leq of up to 15 db(a) in noise-sensitive areas could be achieved, the associated costs being about 10% of the earthworks cost. The paper suggests a procedure for the assessment of road construction noise which employs some of the methods described. /TRRL/