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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Archives of acoustics 2024-09, Vol.49 (4), p.543
Hauptverfasser: Do Nascimento, Eriberto Oliveira, Zannin, Paulo Henrique Trombetta
Format: Artikel
Sprache:eng ; pol
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue 4
container_start_page 543
container_title Archives of acoustics
container_volume 49
creator Do Nascimento, Eriberto Oliveira
Zannin, Paulo Henrique Trombetta
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
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_3149057386</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3149057386</sourcerecordid><originalsourceid>FETCH-LOGICAL-c1111-6cd3d1782d5425b3b59bd19a32d1a914d268154844f99279573b69dcf12fbf633</originalsourceid><addsrcrecordid>eNotUMtKAzEUDaJgqd27DLiemtc8sixtfUBpwSq4GzJ5SOrMpCaZiv_gRxvbns29XM45l3MAuMVoShgj-b1wYkoQYVPMqgrjCzAiFKGMFOT9EowQpmWWozK_BpMQdiiBclJSPgK_c9c1ttcKLg-iHUS0rofOwBfnOjiTbgjRSrjQQXq7j84HaHu4sMZor_sIt9EPMg5etHDuemM_0vpvEeDBCrhZLDdruLXd0J6voldw5qM1VtqkWeujdK3jt_Of4QZcGdEGPTnPMXh7WL7On7LV5vF5PltlEidkhVRU4bIiKk_ZG9rkvFGYC0oUFhwzRYoK56xizPAUk-clbQqupMHENKagdAzuTr57774GHWK9c4Pv08uaYsZRElRFYqETS3oXgtem3nvbCf9TY1Qfa69T7fV_7fWpdvoHDw928g</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3149057386</pqid></control><display><type>article</type><title>Combined Evaluation of Room Acoustic Descriptors in Different Structural Configurations via ODEON Simulations and Artificial Neural Networks</title><source>EZB-FREE-00999 freely available EZB journals</source><creator>Do Nascimento, Eriberto Oliveira ; Zannin, Paulo Henrique Trombetta</creator><creatorcontrib>Do Nascimento, Eriberto Oliveira ; Zannin, Paulo Henrique Trombetta</creatorcontrib><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 &lt; 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.</description><identifier>ISSN: 0137-5075</identifier><identifier>EISSN: 2300-262X</identifier><identifier>DOI: 10.24425/aoa.2024.148811</identifier><language>eng ; pol</language><publisher>Warsaw: Polish Academy of Sciences</publisher><subject>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</subject><ispartof>Archives of acoustics, 2024-09, Vol.49 (4), p.543</ispartof><rights>2024. This work is licensed under https://creativecommons.org/licenses/by-sa/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Do Nascimento, Eriberto Oliveira</creatorcontrib><creatorcontrib>Zannin, Paulo Henrique Trombetta</creatorcontrib><title>Combined Evaluation of Room Acoustic Descriptors in Different Structural Configurations via ODEON Simulations and Artificial Neural Networks</title><title>Archives of acoustics</title><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 &lt; 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.</description><subject>Absorptivity</subject><subject>Acoustics</subject><subject>Artificial neural networks</subject><subject>Background noise</subject><subject>Design factors</subject><subject>Evaluation</subject><subject>Factorial design</subject><subject>In situ measurement</subject><subject>Neural networks</subject><subject>Profile method (forecasting)</subject><subject>Reverberation time</subject><subject>Sensitivity analysis</subject><subject>Sound transmission</subject><issn>0137-5075</issn><issn>2300-262X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNotUMtKAzEUDaJgqd27DLiemtc8sixtfUBpwSq4GzJ5SOrMpCaZiv_gRxvbns29XM45l3MAuMVoShgj-b1wYkoQYVPMqgrjCzAiFKGMFOT9EowQpmWWozK_BpMQdiiBclJSPgK_c9c1ttcKLg-iHUS0rofOwBfnOjiTbgjRSrjQQXq7j84HaHu4sMZor_sIt9EPMg5etHDuemM_0vpvEeDBCrhZLDdruLXd0J6voldw5qM1VtqkWeujdK3jt_Of4QZcGdEGPTnPMXh7WL7On7LV5vF5PltlEidkhVRU4bIiKk_ZG9rkvFGYC0oUFhwzRYoK56xizPAUk-clbQqupMHENKagdAzuTr57774GHWK9c4Pv08uaYsZRElRFYqETS3oXgtem3nvbCf9TY1Qfa69T7fV_7fWpdvoHDw928g</recordid><startdate>20240926</startdate><enddate>20240926</enddate><creator>Do Nascimento, Eriberto Oliveira</creator><creator>Zannin, Paulo Henrique Trombetta</creator><general>Polish Academy of Sciences</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7U5</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>H8D</scope><scope>HCIFZ</scope><scope>L7M</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope></search><sort><creationdate>20240926</creationdate><title>Combined Evaluation of Room Acoustic Descriptors in Different Structural Configurations via ODEON Simulations and Artificial Neural Networks</title><author>Do Nascimento, Eriberto Oliveira ; Zannin, Paulo Henrique Trombetta</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1111-6cd3d1782d5425b3b59bd19a32d1a914d268154844f99279573b69dcf12fbf633</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng ; pol</language><creationdate>2024</creationdate><topic>Absorptivity</topic><topic>Acoustics</topic><topic>Artificial neural networks</topic><topic>Background noise</topic><topic>Design factors</topic><topic>Evaluation</topic><topic>Factorial design</topic><topic>In situ measurement</topic><topic>Neural networks</topic><topic>Profile method (forecasting)</topic><topic>Reverberation time</topic><topic>Sensitivity analysis</topic><topic>Sound transmission</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Do Nascimento, Eriberto Oliveira</creatorcontrib><creatorcontrib>Zannin, Paulo Henrique Trombetta</creatorcontrib><collection>CrossRef</collection><collection>Electronics &amp; Communications Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Aerospace Database</collection><collection>SciTech Premium Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Archives of acoustics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Do Nascimento, Eriberto Oliveira</au><au>Zannin, Paulo Henrique Trombetta</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Combined Evaluation of Room Acoustic Descriptors in Different Structural Configurations via ODEON Simulations and Artificial Neural Networks</atitle><jtitle>Archives of acoustics</jtitle><date>2024-09-26</date><risdate>2024</risdate><volume>49</volume><issue>4</issue><spage>543</spage><pages>543-</pages><issn>0137-5075</issn><eissn>2300-262X</eissn><abstract>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 &lt; 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.</abstract><cop>Warsaw</cop><pub>Polish Academy of Sciences</pub><doi>10.24425/aoa.2024.148811</doi><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0137-5075
ispartof Archives of acoustics, 2024-09, Vol.49 (4), p.543
issn 0137-5075
2300-262X
language eng ; pol
recordid cdi_proquest_journals_3149057386
source EZB-FREE-00999 freely available EZB journals
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
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-29T13%3A08%3A18IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Combined%20Evaluation%20of%20Room%20Acoustic%20Descriptors%20in%20Different%20Structural%20Configurations%20via%20ODEON%20Simulations%20and%20Artificial%20Neural%20Networks&rft.jtitle=Archives%20of%20acoustics&rft.au=Do%20Nascimento,%20Eriberto%20Oliveira&rft.date=2024-09-26&rft.volume=49&rft.issue=4&rft.spage=543&rft.pages=543-&rft.issn=0137-5075&rft.eissn=2300-262X&rft_id=info:doi/10.24425/aoa.2024.148811&rft_dat=%3Cproquest_cross%3E3149057386%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3149057386&rft_id=info:pmid/&rfr_iscdi=true