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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2023-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2023/3615137 |
| _version_ | 1797809198478655488 |
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| author | Ali Othman Albaji Rozeha Bt. A. Rashid Siti Zeleha Abdul Hamid |
| author_facet | Ali Othman Albaji Rozeha Bt. A. Rashid Siti Zeleha Abdul Hamid |
| author_sort | Ali Othman Albaji |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-13T06:48:56Z |
| format | Article |
| id | doaj.art-e2c20e84d501423c8e8165c42b27671c |
| institution | Directory Open Access Journal |
| issn | 2090-0155 |
| language | English |
| last_indexed | 2024-03-13T06:48:56Z |
| publishDate | 2023-01-01 |
| publisher | Hindawi Limited |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj.art-e2c20e84d501423c8e8165c42b27671c2023-06-08T00:00:04ZengHindawi LimitedJournal of Electrical and Computer Engineering2090-01552023-01-01202310.1155/2023/3615137Investigation on Machine Learning Approaches for Environmental Noise ClassificationsAli Othman Albaji0Rozeha Bt. A. Rashid1Siti Zeleha Abdul Hamid2Telecommunication Software and Systems (TeSS) Research GroupTelecommunication Software and Systems (TeSS) Research GroupTelecommunication Software and Systems (TeSS) Research GroupThis 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.http://dx.doi.org/10.1155/2023/3615137 |
| spellingShingle | Ali Othman Albaji Rozeha Bt. A. Rashid Siti Zeleha Abdul Hamid Investigation on Machine Learning Approaches for Environmental Noise Classifications Journal of Electrical and Computer Engineering |
| title | 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_short | Investigation on Machine Learning Approaches for Environmental Noise Classifications |
| title_sort | investigation on machine learning approaches for environmental noise classifications |
| url | http://dx.doi.org/10.1155/2023/3615137 |
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