IoT-based sound-level control for audio amplifiers: mosques as a case study

<p><span>When using audio-amplifiers in the open, uneven distribution of sound makes people unpleasant because it is loud or unheared. This unfortunate situation arises because audio-amplifiers volumes are set according to the guess of sound technicians. Mosques, as an example, are distributed inside wide areas and fire Azan five times a day. Due to the relatively long distances between them, speed and direction of the wind impose setting sound levels prior to each Azan such that all the area is covered and the overlap is minimized. In this paper, we propose a system based on internet of things (IoT) model to control the sound level of each mosque in the community. An IoT device (one in a mosque) sets the level of sound fired by the audio-amplifier. To do that, a synchronized series of tones is fired from each node. Once a node hears these tones, the process of sound level control starts to indicate the distances to heared nodes. As the approximate distances between nodes are known, each node can calculate its suitable sound level. Results showed that the proposed system is effective in setting sound levels for mosques audio amplifiers.</span></p>

Noise Pollution Monitoring at Some Indoor Environments in Basrah City, Iraq

Noise is an unavoidable stressor nowadays; it adversely affects human health and the ability to perform mental and complex tasks. Eight selected points representing home environment were sampled in urban zones, the indoor and outdoor noise levels in these residential areas were evaluated during the summer and winter seasons. Also, nine points in educational institute represented by the College of Science through its lecture halls, laboratories and corridors, to compare them with WHO limits of sound pressure levels, to assess the level of noise experienced by people in indoor closed environments. The current study results showed higher sound levels than required inside homes and educational places. For homes, the highest sound level was 73.9 dB during summer in the living room and the lowest was 42.1 in winter in the bedroom, while the highest sound level in the educational environment was 72.37 dB in summer and the lowest was 61.75 dB in winter. Exposure of individuals to high levels of noise in educational institutions and in their homes means longer hours of exposure to noise pollution, which will negatively affect their health. Thus there is a serious need nowadays to adopt suitable control to reduce noise pollution at indoor points.

A Comparative Study on Noise Pollution Levels In Dhanmondi Area, Dhaka: A Geospatial Analysis

Noise pollution has been recognized as one of the most vital environmental pollutions that affecting urban area’s quality of life. Sound levels at different points of the study area have been recorded and presented spatially by geostatistical analysis. A comparison has been made between the study area data in 2019 and that of 2021. In 2021 the noise level was significantly higher despite the Covid 19 pandemic. The comparative study showed that the noise level of the study area in 2021increased significantly. Noise level data in the sample points of the study area on Friday (holiday) were also collected and found that it was higher than that of even working days, especially in the evening. J. Asiat. Soc. Bangladesh, Sci. 47(2): 99-108, December 2021

Towards the Interpretation of Sound Measurements from Smartphones Collected with Mobile Crowdsensing in the Healthcare Domain: An Experiment with Android Devices

The ubiquity of mobile devices fosters the combined use of ecological momentary assessments (EMA) and mobile crowdsensing (MCS) in the field of healthcare. This combination not only allows researchers to collect ecologically valid data, but also to use smartphone sensors to capture the context in which these data are collected. The TrackYourTinnitus (TYT) platform uses EMA to track users’ individual subjective tinnitus perception and MCS to capture an objective environmental sound level while the EMA questionnaire is filled in. However, the sound level data cannot be used directly among the different smartphones used by TYT users, since uncalibrated raw values are stored. This work describes an approach towards making these values comparable. In the described setting, the evaluation of sensor measurements from different smartphone users becomes increasingly prevalent. Therefore, the shown approach can be also considered as a more general solution as it not only shows how it helped to interpret TYT sound level data, but may also stimulate other researchers, especially those who need to interpret sensor data in a similar setting. Altogether, the approach will show that measuring sound levels with mobile devices is possible in healthcare scenarios, but there are many challenges to ensuring that the measured values are interpretable.

Quantification of Sound Exposure from Wind Turbines in France

The WHO guidelines on environmental noise highlight that evidence on the health effects of wind turbine sound levels is either non-existent or of poor quality. In this context, a feasibility study was conducted in France in 2017. The objective was to suggest a methodology for calculating wind turbine sound levels in order to quantify the number of windfarms’ residents exposed to this sound. Based on a literature review, the Harmonoise model was selected for sound exposure calculation. It was validated by quantifying its uncertainties, and finally used to estimate the population exposed to wind turbine sound in metropolitan France. Compared to other environmental noise sources (e.g., transportation), sound exposure is very moderate, with more than 80% of the exposed people exposed to sound levels below 40 dBA. The total number of people exposed to more than 30 dBA is about 686,000 and 722,000 people for typical daytime and night-time meteorological conditions respectively, i.e., about 1% of the French population in 2017. These results represent the first ever assessment of sound exposure from wind turbines at the scale of the entire metropolitan France.

Patterns of Delirium in a Pediatric Intensive Care Unit and Associations With Noise Pollution

Objective Delirium is a common problem in the Pediatric Intensive Care Unit (PICU) and is associated with increased length of stay, cost and mortality. This study evaluated the relationship between noise pollution and delirium risk. Design: This is a Quality Improvement (QI) initiative at an academic PICU. Sound levels were monitored and patients were screened for delirium using the Cornell Assessment of Pediatric Delirium (CAPD). Setting PICU Patients: All PICU patients Interventions: None Measurements and Main Results: Over the 83-week study period (2015-2017), the median [IQR] CAPD score was 8 [3 to 14]. Nursing compliance with the CAPD was 72.2%. The proportion of patients screening positive for delirium (CAPD ≥ 9) was 45.9%. A total of 329 711 hly decibel (dB) measurements were collected and reported. Occupied rooms were louder than unoccupied rooms (51.8 [51.6-51.9] dB vs. 49.8 [49.7-49.9] dB, respectively, p < 0.001). Days (10 AM to 4 PM) were louder than nights (11 PM to 5 AM) (52.8 [52.7-53.0] dB vs. 50.7 [49.9-51.5] dB, respectively p < 0.001) in occupied rooms. Winter (Nov-Feb) months were louder than summer (May-Aug) months (52.0 [51.8-52.3] dB vs. 51.5 [51.3-51.7] dB, respectively, p < 0.002) in occupied rooms. Median weekly nighttime noise levels and CAPD scores demonstrated a correlation coefficient of 0.6 ( p < 0.001). Median weekly risk of mortality (ROM) and CAPD scores demonstrated a correlation coefficient of 0.15 ( p < 0.01). Conclusions: Significant noise pollution exists in the PICU with a moderate correlation between nighttime noise levels and CAPD scores. This could potentially implicate noise pollution as a risk factor for the development of delirium.

The effect of animal shelter sound on cat behaviour and welfare

This study assessed how sound affected fear- and maintenance-related behaviour in singly housed cats (Felis silvestris catus) in an animal shelter. Two daily 30-min observation sessions (morning and evening) were made for 98 cats from admittance for ten days or until the cat was removed. Cat behaviour and presence of sound (classified by the source) were recorded by instantaneous and onezero sampling with 15-s intervals. Each 30-min observation session was classified as 'quiet' or 'noisy' if the one-zero score for presence of sound was above or below the median of sessions at that time of day. To ensure that cats had at least two complete days of comparable observations, statistical analysis was restricted to the 70 cats (30 females, 40 males) present for two or more weekdays. Cats varied widely in the amount of fear and maintenance behaviour they performed. Males showed less fear and maintenance behaviour than females. Morning sessions consistently had much more sound than evenings, and cats showed more fear behaviour and less maintenance behaviour in the mornings. Cats showed more fear behaviour in noisy morning sessions than quiet ones, with no comparable difference in maintenance behaviour. Where sessions included a pronounced transition in sound, fear-related behaviour was more common after a transition from quiet to noisy and less common after a transition from noisy to quiet. The results show that shelter cats vary greatly in their responses and suggest that sound in shelter environments can substantially affect their behaviour. Lowering sound levels in shelters may help improve cat welfare.

Large Vessel Activity and Low-Frequency Underwater Sound Benchmarks in United States Waters

Chronic low-frequency noise from commercial shipping is a worldwide threat to marine animals that rely on sound for essential life functions. Although the U.S. National Oceanic and Atmospheric Administration recognizes the potential negative impacts of shipping noise in marine environments, there are currently no standard metrics to monitor and quantify shipping noise in U.S. marine waters. However, one-third octave band acoustic measurements centered at 63 and 125 Hz are used as international (European Union Marine Strategy Framework Directive) indicators for underwater ambient noise levels driven by shipping activity. We apply these metrics to passive acoustic monitoring data collected over 20 months in 2016–2017 at five dispersed sites throughout the U.S. Exclusive Economic Zone: Alaskan Arctic, Hawaii, Gulf of Mexico, Northeast Canyons and Seamounts Marine National Monument (Northwest Atlantic), and Cordell Bank National Marine Sanctuary (Northeast Pacific). To verify the relationship between shipping activity and underwater sound levels, vessel movement data from the Automatic Identification System (AIS) were paired to each passive acoustic monitoring site. Daily average sound levels were consistently near to or higher than 100 dB re 1 μPa in both the 63 and 125 Hz one-third octave bands at sites with high levels of shipping traffic (Gulf of Mexico, Northeast Canyons and Seamounts, and Cordell Bank). Where cargo vessels were less common (the Arctic and Hawaii), daily average sound levels were comparatively lower. Specifically, sound levels were ∼20 dB lower year-round in Hawaii and ∼10-20 dB lower in the Alaskan Arctic, depending on the season. Although these band-level measurements can only generally facilitate differentiation of sound sources, these results demonstrate that international acoustic indicators of commercial shipping can be applied to data collected in U.S. waters as a unified metric to approximate the influence of shipping as a driver of ambient noise levels, provide critical information to managers and policy makers about the status of marine environments, and to identify places and times for more detailed investigation regarding environmental impacts.

The Correlation Between the Statistical Pass-By Index Values and the Total Number of Vehicle Passes

In this study, the traffic noise degradation in asphalt pavements was analysed using the ‘Statistical Pass-By method’. The sound levels of two surfaces were monitored during 9 and 12 years of service, respectively. By comparing the dependencies of the maximum A-weighted sound pressure level on logarithm of vehicle velocity, an increase in the sound level was found at all recorded speeds. Following an analysis of sound levels, as combined with the statistical pass-by index (SPBI) calculated versus age (expressed in vehicles), it was determined that the noise is an increasing power function of SPBI values on vehicle passes, based on an approximation of noise level adjustment to a reference temperature of 20 °C (using a coefficient of 0.06 for asphalt concrete surface AC11 and - 0.03 for mastic asphalt SMA11). The adjusted traffic noise degradation model showed that the SMA11 surface has a higher resistance to acoustic degradation than AC11 surface.