Prediction of classroom reverberation time using neural network
In this paper, an alternative method for predicting the reverberation time (RT) using neural network (NN) for classroom was designed and explored. Classroom models were created using Google SketchUp software. The NN applied training dataset from the classroom models with RT values that were computed...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2018
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| Online Access: | http://eprints.uthm.edu.my/5555/ http://eprints.uthm.edu.my/5555/1/AJ%202018%20%28214%29.pdf |
| _version_ | 1848888581833097216 |
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| author | Zainudin, Fathin Liyana Mahamad, Abd Kadir Saon, Sharifah Yahya, Musli Nizam |
| author_facet | Zainudin, Fathin Liyana Mahamad, Abd Kadir Saon, Sharifah Yahya, Musli Nizam |
| author_sort | Zainudin, Fathin Liyana |
| building | UTHM Institutional Repository |
| collection | Online Access |
| description | In this paper, an alternative method for predicting the reverberation time (RT) using neural network (NN) for classroom was designed and explored. Classroom models were created using Google SketchUp software. The NN applied training dataset from the classroom models with RT values that were computed from ODEON 12.10 software. The NN was conducted separately for 500Hz, 1000Hz, and 2000Hz as absorption coefficient that is one of the prominent input variable is frequency dependent. Mean squared error (MSE) and regression (R) values were obtained to examine the NN efficiency. Overall, the NN shows a good result with MSE < 0.005 and R > 0.9. The NN also managed to achieve a percentage of accuracy of 92.53% for 500Hz, 93.66% for 1000Hz, and 93.18% for 2000Hz and thus displays a good and efficient performance. Nevertheless, the optimum RT value is range between 0.75 – 0.9 seconds. |
| first_indexed | 2025-11-15T20:12:34Z |
| format | Article |
| id | uthm-5555 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:12:34Z |
| publishDate | 2018 |
| publisher | IOP Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-55552022-01-17T01:07:15Z http://eprints.uthm.edu.my/5555/ Prediction of classroom reverberation time using neural network Zainudin, Fathin Liyana Mahamad, Abd Kadir Saon, Sharifah Yahya, Musli Nizam T Technology (General) TK Electrical engineering. Electronics Nuclear engineering QC501-(721) Electricity In this paper, an alternative method for predicting the reverberation time (RT) using neural network (NN) for classroom was designed and explored. Classroom models were created using Google SketchUp software. The NN applied training dataset from the classroom models with RT values that were computed from ODEON 12.10 software. The NN was conducted separately for 500Hz, 1000Hz, and 2000Hz as absorption coefficient that is one of the prominent input variable is frequency dependent. Mean squared error (MSE) and regression (R) values were obtained to examine the NN efficiency. Overall, the NN shows a good result with MSE < 0.005 and R > 0.9. The NN also managed to achieve a percentage of accuracy of 92.53% for 500Hz, 93.66% for 1000Hz, and 93.18% for 2000Hz and thus displays a good and efficient performance. Nevertheless, the optimum RT value is range between 0.75 – 0.9 seconds. IOP Publishing 2018 Article PeerReviewed text en http://eprints.uthm.edu.my/5555/1/AJ%202018%20%28214%29.pdf Zainudin, Fathin Liyana and Mahamad, Abd Kadir and Saon, Sharifah and Yahya, Musli Nizam (2018) Prediction of classroom reverberation time using neural network. Journal of Physics: Conference Series, 995. pp. 1-8. ISSN 1742-6588 |
| spellingShingle | T Technology (General) TK Electrical engineering. Electronics Nuclear engineering QC501-(721) Electricity Zainudin, Fathin Liyana Mahamad, Abd Kadir Saon, Sharifah Yahya, Musli Nizam Prediction of classroom reverberation time using neural network |
| title | Prediction of classroom reverberation time using neural network |
| title_full | Prediction of classroom reverberation time using neural network |
| title_fullStr | Prediction of classroom reverberation time using neural network |
| title_full_unstemmed | Prediction of classroom reverberation time using neural network |
| title_short | Prediction of classroom reverberation time using neural network |
| title_sort | prediction of classroom reverberation time using neural network |
| topic | T Technology (General) TK Electrical engineering. Electronics Nuclear engineering QC501-(721) Electricity |
| url | http://eprints.uthm.edu.my/5555/ http://eprints.uthm.edu.my/5555/1/AJ%202018%20%28214%29.pdf |