Combined Evaluation of Room Acoustic Descriptors in Different Structural Configurations via ODEON Simulations and Artificial Neural Networks
This study evaluated the combined sensitivity analysis of several room acoustic descriptors: reverberation time (T30), center time (Ts), early decay time (EDT), definition (D50), clarity (C50), useful-to-detrimental sound ratio (U50), and speech transmission index (STI); and also it assessed how the...
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Veröffentlicht in: | Archives of acoustics 2024-09, Vol.49 (4), p.543 |
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description | This study evaluated the combined sensitivity analysis of several room acoustic descriptors: reverberation time (T30), center time (Ts), early decay time (EDT), definition (D50), clarity (C50), useful-to-detrimental sound ratio (U50), and speech transmission index (STI); and also it assessed how these descriptors responded jointly to different acoustic-structural factors. The first-order factors were background noise (A), acoustic ceiling tile sound absorption coefficient (B), confinement (C), and occupancy (D), along with their interaction effects. A novel method is proposed for this joint evaluation of sensitivity factors. This method involves in situ measurements and an unreplicated 24 factorial design, which has been validated by ODEON software. The significance of input factors is determined using artificial neural networks (ANN) and the modified profile method (MPM), validated by multiple linear regression (MLR). Three significant correlation groups are identified at p < 0:05: group 1 (EDT, T30, Ts), group 2 (C50, D50), and group 3 (U50, STI). The ceiling material sound absorption (B) is found to affect reverberation (groups 1 and 2), while background noise (A) impacts STI and U50. A weak correlation is found between D50 and STI. These results are confirmed by the MLR and MPM methods. |
doi_str_mv | 10.24425/aoa.2024.148811 |
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The first-order factors were background noise (A), acoustic ceiling tile sound absorption coefficient (B), confinement (C), and occupancy (D), along with their interaction effects. A novel method is proposed for this joint evaluation of sensitivity factors. This method involves in situ measurements and an unreplicated 24 factorial design, which has been validated by ODEON software. The significance of input factors is determined using artificial neural networks (ANN) and the modified profile method (MPM), validated by multiple linear regression (MLR). Three significant correlation groups are identified at p < 0:05: group 1 (EDT, T30, Ts), group 2 (C50, D50), and group 3 (U50, STI). The ceiling material sound absorption (B) is found to affect reverberation (groups 1 and 2), while background noise (A) impacts STI and U50. A weak correlation is found between D50 and STI. 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The first-order factors were background noise (A), acoustic ceiling tile sound absorption coefficient (B), confinement (C), and occupancy (D), along with their interaction effects. A novel method is proposed for this joint evaluation of sensitivity factors. This method involves in situ measurements and an unreplicated 24 factorial design, which has been validated by ODEON software. The significance of input factors is determined using artificial neural networks (ANN) and the modified profile method (MPM), validated by multiple linear regression (MLR). Three significant correlation groups are identified at p < 0:05: group 1 (EDT, T30, Ts), group 2 (C50, D50), and group 3 (U50, STI). The ceiling material sound absorption (B) is found to affect reverberation (groups 1 and 2), while background noise (A) impacts STI and U50. A weak correlation is found between D50 and STI. 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and also it assessed how these descriptors responded jointly to different acoustic-structural factors. The first-order factors were background noise (A), acoustic ceiling tile sound absorption coefficient (B), confinement (C), and occupancy (D), along with their interaction effects. A novel method is proposed for this joint evaluation of sensitivity factors. This method involves in situ measurements and an unreplicated 24 factorial design, which has been validated by ODEON software. The significance of input factors is determined using artificial neural networks (ANN) and the modified profile method (MPM), validated by multiple linear regression (MLR). Three significant correlation groups are identified at p < 0:05: group 1 (EDT, T30, Ts), group 2 (C50, D50), and group 3 (U50, STI). The ceiling material sound absorption (B) is found to affect reverberation (groups 1 and 2), while background noise (A) impacts STI and U50. A weak correlation is found between D50 and STI. 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subjects | Absorptivity Acoustics Artificial neural networks Background noise Design factors Evaluation Factorial design In situ measurement Neural networks Profile method (forecasting) Reverberation time Sensitivity analysis Sound transmission |
title | Combined Evaluation of Room Acoustic Descriptors in Different Structural Configurations via ODEON Simulations and Artificial Neural Networks |
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