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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| Format: | Article |
| Language: | English |
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Institute of Advanced Engineering and Science
2022
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| Online Access: | http://eprints.utm.my/104456/1/RozehaARashid2022_AMachineLearningforEnvironmentalNoise.pdf |
| _version_ | 1796867661034946560 |
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| 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 |
| collection | ePrints |
| 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. |
| first_indexed | 2024-03-05T21:30:37Z |
| format | Article |
| id | utm.eprints-104456 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T21:30:37Z |
| publishDate | 2022 |
| publisher | Institute of Advanced Engineering and Science |
| record_format | dspace |
| spelling | utm.eprints-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 |
| 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 |
| title | 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_short | A machine learning for environmental noise classification in smart cities |
| title_sort | machine learning for environmental noise classification in smart cities |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://eprints.utm.my/104456/1/RozehaARashid2022_AMachineLearningforEnvironmentalNoise.pdf |
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