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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Institute of Fundamental Technological Research Polish Academy of Sciences
2024-09-01
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| Series: | Archives of Acoustics |
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| Online Access: | https://acoustics.ippt.pan.pl/index.php/aa/article/view/3917 |
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| author | Eriberto Oliveira DO NASCIMENTO Paulo Henrique Trombetta ZANNIN |
| author_facet | Eriberto Oliveira DO NASCIMENTO Paulo Henrique Trombetta ZANNIN |
| author_sort | Eriberto Oliveira DO NASCIMENTO |
| collection | DOAJ |
| 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 2^4 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. |
| format | Article |
| id | doaj-art-0e9c836b93fe41c0907705f5bbc7c624 |
| institution | Kabale University |
| issn | 0137-5075 2300-262X |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Institute of Fundamental Technological Research Polish Academy of Sciences |
| record_format | Article |
| series | Archives of Acoustics |
| spelling | doaj-art-0e9c836b93fe41c0907705f5bbc7c6242025-08-20T03:58:10ZengInstitute of Fundamental Technological Research Polish Academy of SciencesArchives of Acoustics0137-50752300-262X2024-09-0149410.24425/aoa.2024.148811Combined Evaluation of Room Acoustic Descriptors in Different Structural Configurations via ODEON Simulations and Artificial Neural NetworksEriberto Oliveira DO NASCIMENTO0Paulo Henrique Trombetta ZANNIN1Laboratory of Environmental and Industrial Acoustics and Acoustic Comfort Federal University of Paraná – UFPRLaboratory of Environmental and Industrial Acoustics and Acoustic Comfort Federal University of Paraná – UFPRThis 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 2^4 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.https://acoustics.ippt.pan.pl/index.php/aa/article/view/3917speech transmission indexreverberation timeartificial neural networksroom acousticsODEON simulation |
| spellingShingle | Eriberto Oliveira DO NASCIMENTO Paulo Henrique Trombetta ZANNIN Combined Evaluation of Room Acoustic Descriptors in Different Structural Configurations via ODEON Simulations and Artificial Neural Networks Archives of Acoustics speech transmission index reverberation time artificial neural networks room acoustics ODEON simulation |
| title | Combined Evaluation of Room Acoustic Descriptors in Different Structural Configurations via ODEON Simulations and Artificial Neural Networks |
| title_full | Combined Evaluation of Room Acoustic Descriptors in Different Structural Configurations via ODEON Simulations and Artificial Neural Networks |
| title_fullStr | Combined Evaluation of Room Acoustic Descriptors in Different Structural Configurations via ODEON Simulations and Artificial Neural Networks |
| title_full_unstemmed | Combined Evaluation of Room Acoustic Descriptors in Different Structural Configurations via ODEON Simulations and Artificial Neural Networks |
| title_short | Combined Evaluation of Room Acoustic Descriptors in Different Structural Configurations via ODEON Simulations and Artificial Neural Networks |
| title_sort | combined evaluation of room acoustic descriptors in different structural configurations via odeon simulations and artificial neural networks |
| topic | speech transmission index reverberation time artificial neural networks room acoustics ODEON simulation |
| url | https://acoustics.ippt.pan.pl/index.php/aa/article/view/3917 |
| work_keys_str_mv | AT eribertooliveiradonascimento combinedevaluationofroomacousticdescriptorsindifferentstructuralconfigurationsviaodeonsimulationsandartificialneuralnetworks AT paulohenriquetrombettazannin combinedevaluationofroomacousticdescriptorsindifferentstructuralconfigurationsviaodeonsimulationsandartificialneuralnetworks |