Using machine learning to predict indoor acoustic indicators of multi-functional activity centers

Chiu Yu Yeh, Yaw Shyan Tsay

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


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 JND ± 2, and the JND of C50, C80, STI, and the distribution of SPL could reach within ±1. Through machine learning meth-ods, 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.

Original languageEnglish
Article number5641
JournalApplied Sciences (Switzerland)
Issue number12
Publication statusPublished - 2021 Jun 2

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes


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