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A machine learning for environmental noise classification in smart cities

The sound at the same decibel (dB) level may be perceived either as annoying noise or as pleasant music. Therefore, it is necessary to go beyond the state-of-the-art approaches that measure only the dB level and also identify the type of the sound especially when the sound is recorded using a microp...

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Main Authors: Ali, Yaseen Hadi, A. Rashid, Rozeha, Abdul Hamid, Siti Zaleha
Format: Article
Language:English
Published: Institute of Advanced Engineering and Science 2022
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/104456/1/RozehaARashid2022_AMachineLearningforEnvironmentalNoise.pdf
http://eprints.utm.my/104456/
http://dx.doi.org/10.11591/ijeecs.v25.i3.pp1777-1786
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spelling my.utm.1044562024-02-08T08:01:53Z http://eprints.utm.my/104456/ A machine learning for environmental noise classification in smart cities Ali, Yaseen Hadi A. Rashid, Rozeha Abdul Hamid, Siti Zaleha TK Electrical engineering. Electronics Nuclear engineering The sound at the same decibel (dB) level may be perceived either as annoying noise or as pleasant music. Therefore, it is necessary to go beyond the state-of-the-art approaches that measure only the dB level and also identify the type of the sound especially when the sound is recorded using a microphone. This paper presented a case study that considers the ability of machine learning models to identify sources of environmental noise in urban areas and compares the sound levels with the recommended levels by the World Health Organization (WHO). The approach was evaluated with a dataset of 44 sound samples grouped in four sound classes that are highway, railway, lawnmowers, and birds. We used mel-frequency cepstral coefficients for feature extraction and supervised algorithms that are Support vector machine (SVM), k-nearest neighbors (KNN), bootstrap aggregation (Bagging), and random forest (RF) for noise classification. We evaluated performance of the four algorithms to determine the best one for the classification of sound samples in the data set under consideration. The findings showed that the noise classification accuracy is in the range of 95%-100%. Furthermore, all the captured data exceeded the recommended levels by WHO which can cause adverse health effects. Institute of Advanced Engineering and Science 2022 Article PeerReviewed application/pdf en http://eprints.utm.my/104456/1/RozehaARashid2022_AMachineLearningforEnvironmentalNoise.pdf Ali, Yaseen Hadi and A. Rashid, Rozeha and Abdul Hamid, Siti Zaleha (2022) A machine learning for environmental noise classification in smart cities. Indonesian Journal of Electrical Engineering and Computer Science, 25 (3). pp. 1777-1786. ISSN 2502-4752 http://dx.doi.org/10.11591/ijeecs.v25.i3.pp1777-1786 DOI : 10.11591/ijeecs.v25.i3.pp1777-1786
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Ali, Yaseen Hadi
A. Rashid, Rozeha
Abdul Hamid, Siti Zaleha
A machine learning for environmental noise classification in smart cities
description The sound at the same decibel (dB) level may be perceived either as annoying noise or as pleasant music. Therefore, it is necessary to go beyond the state-of-the-art approaches that measure only the dB level and also identify the type of the sound especially when the sound is recorded using a microphone. This paper presented a case study that considers the ability of machine learning models to identify sources of environmental noise in urban areas and compares the sound levels with the recommended levels by the World Health Organization (WHO). The approach was evaluated with a dataset of 44 sound samples grouped in four sound classes that are highway, railway, lawnmowers, and birds. We used mel-frequency cepstral coefficients for feature extraction and supervised algorithms that are Support vector machine (SVM), k-nearest neighbors (KNN), bootstrap aggregation (Bagging), and random forest (RF) for noise classification. We evaluated performance of the four algorithms to determine the best one for the classification of sound samples in the data set under consideration. The findings showed that the noise classification accuracy is in the range of 95%-100%. Furthermore, all the captured data exceeded the recommended levels by WHO which can cause adverse health effects.
format Article
author Ali, Yaseen Hadi
A. Rashid, Rozeha
Abdul Hamid, Siti Zaleha
author_facet Ali, Yaseen Hadi
A. Rashid, Rozeha
Abdul Hamid, Siti Zaleha
author_sort Ali, Yaseen Hadi
title A machine learning for environmental noise classification in smart cities
title_short A machine learning for environmental noise classification in smart cities
title_full A machine learning for environmental noise classification in smart cities
title_fullStr A machine learning for environmental noise classification in smart cities
title_full_unstemmed A machine learning for environmental noise classification in smart cities
title_sort machine learning for environmental noise classification in smart cities
publisher Institute of Advanced Engineering and Science
publishDate 2022
url http://eprints.utm.my/104456/1/RozehaARashid2022_AMachineLearningforEnvironmentalNoise.pdf
http://eprints.utm.my/104456/
http://dx.doi.org/10.11591/ijeecs.v25.i3.pp1777-1786
_version_ 1792147746355937280
score 13.211869

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