Monitoring Residential Noise for Prospective Home Owners and Renters

Zimmerman, Thomas; Robson, Christine

doi:10.1007/978-3-642-21726-5_3

Cited by 9 publications

(5 citation statements)

References 7 publications

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“…Noise levels within the sampled locations were measured in-situ. This method involved a continuous (real time) monitoring, in which noise data was automatically transmitted and saved into the database of the monitoring device (Yilmaz & Hocanli, 2006, Kanjo, 2010& Zimmerman & Robson, 2011. The results were recorded in the field note.…”

Section: Methods Of Data Collectionmentioning

confidence: 99%

Geo-Spatial Analysis of Noise Levels in a University Community

2022

BJES

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Noise is identified as a disturbance that could induce different behavioural responses. Its exposure for a long period of time has been an issue of concern. Increasing noise levels is not unusual especially with booming developments and innovations in the University environment. This study investigated noise pollution level (NPL) and its spatial distribution within the Campus of Federal University of Technology, Owerri Nigeria. A digital sound level meter-Extech 407730 was used to measure noise level at the sampling points namely, Student affairs unit, SEET head round-about, School park, Senate round-about and FUTO market designated as SP1,SP2,SP3,SP4 and SP5 respectively. A global positioning system (GPS)-NAVA 300 was used to record the GPS coordinates of the sampling noise hotspots in the university community. The Arc GIS software was used to assess the geospatial mapping and distribution of noise within the study areas. Results revealed that the noise levels in the school campus reached a peak of 75.7dB (A) in FUTO market on day 4 during the hours of 12pm – 1pm. Results of geospatial mapping of the noise level in the study area also revealed that noise level exceeds the maximum permissible limit ranging from 40dB(A) to 50dB(A). Therefore, it was recommended that public enlightenment programs such as workshops and seminars on the health hazards connected with noise pollution within the campus should be held regularly, also, control of noise through the use of sound proof doors, walls, and ceilings that can be installed while erecting buildings within the university community.

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Section: Methods Of Data Collectionmentioning

confidence: 99%

Geo-Spatial Analysis of Noise Levels in a University Community

2022

BJES

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“…Zimmerman and Robson [14] claim that they can send a short message service (SMS) text with the entire day's sound summary in less than a minute using their method. It is straightforward because the proposed microprocessor operates at a low level of cost.…”

Section: Literature Reviewmentioning

confidence: 99%

IoT cloud based noise intensity monitoring system

Fatema

Hakim

Mim

et al. 2023

IJEECS

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According to a survey published by the United Nations Environment Program (UNEP) last month, Dhaka, Bangladesh’s capital, is the world’s noisiest metropolis. Dhaka is consistently ranked as one of the most polluted cities in the world. The World Health Organization (WHO) has set a noise intensity limit of 55 decibels for a certain region. Dhaka, on the other hand, was determined to be twice as loud, at 110 to 132 decibels. As many as 66 percent of traffic cops work on the road to control traffic, and this high noise intensity noise is causing them hearing and sleeping problems on a daily basis. Furthermore, loud noises can increase blood pressure and pulse rates, produce mental stress, and interfere with amusement and personal interactions. Keeping all of this in mind, we designed a real-time noise pollution monitoring system that will assist in conducting experiments in this type of environment by evaluating noise intensity and automatically sending data to a IoT cloud-based platform.

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“…Predicting ambient noise has necessitated research on the spatial propagation of noise. It is possible to design a noise distribution by using the propagation model of noise attenuation [6]. Tere have also been investigations on the most efective locations for noise monitoring stations [5].…”

Section: Previous Workmentioning

confidence: 99%

Investigation on Machine Learning Approaches for Environmental Noise Classifications

ALBAJI

Rashid

Hamid

2023

Journal of Electrical and Computer Engineering

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This project aims to investigate the best machine learning (ML) algorithm for classifying sounds originating from the environment that were considered noise pollution in smart cities. Sound collection was carried out using necessary sound capture tools, after which ML classification models were utilized for sound recognition. Additionally, noise pollution monitoring using Python was conducted to provide accurate results for sixteen different types of noise that were collected in sixteen cities in Malaysia. The numbers on the diagonal represent the correctly classified noises from the test set. Using these correlation matrices, the F1 score was calculated, and a comparison was performed for all models. The best model was found to be random forest.

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Monitoring Residential Noise for Prospective Home Owners and Renters

Cited by 9 publications

References 7 publications

Geo-Spatial Analysis of Noise Levels in a University Community

Geo-Spatial Analysis of Noise Levels in a University Community

IoT cloud based noise intensity monitoring system

Investigation on Machine Learning Approaches for Environmental Noise Classifications

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