Using Machine Learning to Predict Indoor Acoustic Indicators of Multi-Functional Activity Centers
In Taiwan, activity centers such as school auditoriums and gymnasiums are common multi-functional spaces that are often used for performances, singing, and speeches. However, most cases are designed using only Sabine’s equation for architectural acoustics. Although that estimation formula is simple...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-06-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/11/12/5641 |
| _version_ | 1797529733540347904 |
|---|---|
| author | Chiu-Yu Yeh Yaw-Shyan Tsay |
| author_facet | Chiu-Yu Yeh Yaw-Shyan Tsay |
| author_sort | Chiu-Yu Yeh |
| collection | DOAJ |
| description | In Taiwan, activity centers such as school auditoriums and gymnasiums are common multi-functional spaces that are often used for performances, singing, and speeches. However, most cases are designed using only Sabine’s equation for architectural acoustics. Although that estimation formula is simple and fast, the calculation process ignores many details. Furthermore, while more accurate analysis can be obtained through acoustics simulation software, it is more complicated and time-consuming and thus is rarely used in practical design. The purpose of this study is to use machine learning to propose a predictive model of acoustic indicators as a simple evaluation tool for the architectural design and interior decoration of multi-functional activity centers. We generated 800 spaces using parametric design, adopting Odeon to obtain acoustic indicators. The machine learning model was trained with basic information of the space. We found that through GBDT and ANN algorithms, almost all acoustic indicators could be predicted within <i>JND</i> ± 2, and the <i>JND</i> of C50, C80, STI, and the distribution of SPL could reach within ±1. Through machine learning methods, we established a convenient, fast, and accurate prediction model and were able to obtain various acoustic indicators of the space without 3D-modeling or simulation software. |
| first_indexed | 2024-03-10T10:18:01Z |
| format | Article |
| id | doaj.art-e12de44d00404e7ea517a656a627363d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T10:18:01Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-e12de44d00404e7ea517a656a627363d2023-11-22T00:42:23ZengMDPI AGApplied Sciences2076-34172021-06-011112564110.3390/app11125641Using Machine Learning to Predict Indoor Acoustic Indicators of Multi-Functional Activity CentersChiu-Yu Yeh0Yaw-Shyan Tsay1Department of Architecture, National Cheng Kung University, Tainan 701, TaiwanDepartment of Architecture, National Cheng Kung University, Tainan 701, TaiwanIn Taiwan, activity centers such as school auditoriums and gymnasiums are common multi-functional spaces that are often used for performances, singing, and speeches. However, most cases are designed using only Sabine’s equation for architectural acoustics. Although that estimation formula is simple and fast, the calculation process ignores many details. Furthermore, while more accurate analysis can be obtained through acoustics simulation software, it is more complicated and time-consuming and thus is rarely used in practical design. The purpose of this study is to use machine learning to propose a predictive model of acoustic indicators as a simple evaluation tool for the architectural design and interior decoration of multi-functional activity centers. We generated 800 spaces using parametric design, adopting Odeon to obtain acoustic indicators. The machine learning model was trained with basic information of the space. We found that through GBDT and ANN algorithms, almost all acoustic indicators could be predicted within <i>JND</i> ± 2, and the <i>JND</i> of C50, C80, STI, and the distribution of SPL could reach within ±1. Through machine learning methods, we established a convenient, fast, and accurate prediction model and were able to obtain various acoustic indicators of the space without 3D-modeling or simulation software.https://www.mdpi.com/2076-3417/11/12/5641architectural acousticsindoor acoustic indicatorsmulti-functional spacemachine learningsupervised learning |
| spellingShingle | Chiu-Yu Yeh Yaw-Shyan Tsay Using Machine Learning to Predict Indoor Acoustic Indicators of Multi-Functional Activity Centers Applied Sciences architectural acoustics indoor acoustic indicators multi-functional space machine learning supervised learning |
| title | Using Machine Learning to Predict Indoor Acoustic Indicators of Multi-Functional Activity Centers |
| title_full | Using Machine Learning to Predict Indoor Acoustic Indicators of Multi-Functional Activity Centers |
| title_fullStr | Using Machine Learning to Predict Indoor Acoustic Indicators of Multi-Functional Activity Centers |
| title_full_unstemmed | Using Machine Learning to Predict Indoor Acoustic Indicators of Multi-Functional Activity Centers |
| title_short | Using Machine Learning to Predict Indoor Acoustic Indicators of Multi-Functional Activity Centers |
| title_sort | using machine learning to predict indoor acoustic indicators of multi functional activity centers |
| topic | architectural acoustics indoor acoustic indicators multi-functional space machine learning supervised learning |
| url | https://www.mdpi.com/2076-3417/11/12/5641 |
| work_keys_str_mv | AT chiuyuyeh usingmachinelearningtopredictindooracousticindicatorsofmultifunctionalactivitycenters AT yawshyantsay usingmachinelearningtopredictindooracousticindicatorsofmultifunctionalactivitycenters |