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Investigation on machine learning approaches for environmental noise classifications.

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 util...

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Main Authors: Albaji, Ali Othman, A. Rashid, Rozeha, Abdul Hamid, Siti Zeleha
Format: Article
Language:English
Published: Hindawi Limited 2023
Subjects:
TK6570 Mobile Communication System
Online Access:http://eprints.utm.my/106501/1/AliOthmanAlbaji2023_InvestigationonMachineLearningApproachesfor.pdf
http://eprints.utm.my/106501/
http://dx.doi.org/10.1155/2023/3615137
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id my.utm.106501
record_format eprints
spelling my.utm.1065012024-07-09T06:18:34Z http://eprints.utm.my/106501/ Investigation on machine learning approaches for environmental noise classifications. Albaji, Ali Othman A. Rashid, Rozeha Abdul Hamid, Siti Zeleha TK6570 Mobile Communication System 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. Hindawi Limited 2023-05-31 Article PeerReviewed application/pdf en http://eprints.utm.my/106501/1/AliOthmanAlbaji2023_InvestigationonMachineLearningApproachesfor.pdf Albaji, Ali Othman and A. Rashid, Rozeha and Abdul Hamid, Siti Zeleha (2023) Investigation on machine learning approaches for environmental noise classifications. Journal of Electrical and Computer Engineering, 2023 (361513). pp. 1-26. ISSN 2090-0147 http://dx.doi.org/10.1155/2023/3615137 DOI:10.1155/2023/3615137
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 TK6570 Mobile Communication System
spellingShingle TK6570 Mobile Communication System
Albaji, Ali Othman
A. Rashid, Rozeha
Abdul Hamid, Siti Zeleha
Investigation on machine learning approaches for environmental noise classifications.
description 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.
format Article
author Albaji, Ali Othman
A. Rashid, Rozeha
Abdul Hamid, Siti Zeleha
author_facet Albaji, Ali Othman
A. Rashid, Rozeha
Abdul Hamid, Siti Zeleha
author_sort Albaji, Ali Othman
title Investigation on machine learning approaches for environmental noise classifications.
title_short Investigation on machine learning approaches for environmental noise classifications.
title_full Investigation on machine learning approaches for environmental noise classifications.
title_fullStr Investigation on machine learning approaches for environmental noise classifications.
title_full_unstemmed Investigation on machine learning approaches for environmental noise classifications.
title_sort investigation on machine learning approaches for environmental noise classifications.
publisher Hindawi Limited
publishDate 2023
url http://eprints.utm.my/106501/1/AliOthmanAlbaji2023_InvestigationonMachineLearningApproachesfor.pdf
http://eprints.utm.my/106501/
http://dx.doi.org/10.1155/2023/3615137
_version_ 1805880820819296256
score 13.211869

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