Email: murat.basaran@alanya.edu.tr (M.A.B.); akin.oktav@alanya.edu.tr (A.O.)
*Corresponding author
Manuscript received July 21, 2025; accepted August 25, 2025; published October 10, 2025.
Abstract—Concert Hall acoustics are traditionally evaluated through direct measurements or acoustic simulations, both of which require significant resources and expertise. This study bridges a critical gap in architectural acoustics by systematically evaluating 28 Machine Learning (ML) algorithms for predicting frequency-dependent Reverberation Time (RT) and clarity (C80) from geometric features. Unlike prior work, our analysis identifies transition frequencies (250–1000 Hz for RT, 250–500 Hz for C80) where geometric influence shifts, offering actionable insights for preliminary acoustic design and renovation planning. We analyzed data from 58 concert halls, utilizing 10 geometric features and acoustic measurements across six octave bands (125–4000 Hz). 28 different ML algorithms were trained and validated using 5-fold cross-validation, with performance evaluated using the coefficient of determination (R²) and Root Mean Square Error (RMSE). The results demonstrate that geometric attributes explain up to 47% of RT variations at lower frequencies (125–500 Hz) and up to 47% of C80 variations at mid-frequencies (250–1000 Hz). Support Vector Machines (SVMs) and Gaussian process regression (GPR) models showed the best performance for RT and C80 estimation, respectively. These findings provide insights into the frequency-dependent influence of architectural geometry on concert hall acoustics and establish a foundation for ML-assisted acoustic design and renovation planning.
Keywords—Clarity (C80), concert hall acoustics, machine learning, Reverberation Time (RT), room acoustic parameters; architectural acoustics.
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Cite: Murat A. Başaran and Akın Oktav, "Predicting Concert Hall Reverberation Time and Clarity from Geometric Features Using Machine Learning," International Journal of Modeling and Optimization, vol. 15, no. 2, pp. 60-67, 2025.
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
