Predicting vehicle category using psychoacoustic indicators from road traffic pass-by noise
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/1822/88963 |
Resumo: | A set of road traffic pass-by noises containing more than 2000 vehicles was recorded following the Statistical Pass-By (SPB) methodology. Besides the acoustic descriptors, psychoacoustic indicators (loudness, sharpness, roughness, fluctuation strength) were retrieved for each pass-by of three vehicle categories defined in the standard (passenger cars, dual-axles and multi-axles heavy vehicles). A fourth vehicle category, comprised of delivery vans, was also investigated. All psychoacoustic indicators significantly differed among vehicle categories, meaning that not only intensity descriptors but also temporal and spectral features of pass-by noise distinguish those classes. With enough instances and a balanced dataset across groups, a machine-learning classification algorithm was trained with 70% of the dataset to predict vehicle categories using the psychoacoustic indicators. Classification accuracy on the test set reached 72%. Accuracy losses were primarily caused by 25% of the actual passenger cars being misclassified as vans and vice-versa. These results show the potential of using noise features other than uniquely the maximum noise level to classify vehicles in terms of noise perception. In this way, limiting classifications based on visual aspects of vehicle categories may be overcome, increasing the practicality and accuracy of measurements such as the SPB, as vehicle fleets worldwide are more consistently represented. |
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Predicting vehicle category using psychoacoustic indicators from road traffic pass-by noiseTraffic noisePredictionPshycoacousticsEngenharia e Tecnologia::Engenharia CivilCidades e comunidades sustentáveisA set of road traffic pass-by noises containing more than 2000 vehicles was recorded following the Statistical Pass-By (SPB) methodology. Besides the acoustic descriptors, psychoacoustic indicators (loudness, sharpness, roughness, fluctuation strength) were retrieved for each pass-by of three vehicle categories defined in the standard (passenger cars, dual-axles and multi-axles heavy vehicles). A fourth vehicle category, comprised of delivery vans, was also investigated. All psychoacoustic indicators significantly differed among vehicle categories, meaning that not only intensity descriptors but also temporal and spectral features of pass-by noise distinguish those classes. With enough instances and a balanced dataset across groups, a machine-learning classification algorithm was trained with 70% of the dataset to predict vehicle categories using the psychoacoustic indicators. Classification accuracy on the test set reached 72%. Accuracy losses were primarily caused by 25% of the actual passenger cars being misclassified as vans and vice-versa. These results show the potential of using noise features other than uniquely the maximum noise level to classify vehicles in terms of noise perception. In this way, limiting classifications based on visual aspects of vehicle categories may be overcome, increasing the practicality and accuracy of measurements such as the SPB, as vehicle fleets worldwide are more consistently represented.The authors thank the Research Foundation – Flanders (FWO) for the travel grant allocated to Ablenya Barros (file ID K149723N).Acoustical Society of America (ASA)Universidade do MinhoBarros, AblenyaGeluykens, MichielPereira, FredericoGoubert, LucFreitas, E. F.Vuye, Cedric2023-032023-03-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/88963engAblenya Barros, Michiel Geluykens, Frederico Pereira, Luc Goubert, Elisabete Freitas, Cedric Vuye; Predicting vehicle category using psychoacoustic indicators from road traffic pass-by noise. Proc. Mtgs. Acoust. 8 May 2023; 51 (1): 040001. https://doi.org/10.1121/2.00017751939-800X10.1121/2.0001775040001https://pubs.aip.org/asa/poma/article/51/1/040001/2908153/Predicting-vehicle-category-using-psychoacousticinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-24T01:25:56Zoai:repositorium.sdum.uminho.pt:1822/88963Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:11:09.227436Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Predicting vehicle category using psychoacoustic indicators from road traffic pass-by noise |
title |
Predicting vehicle category using psychoacoustic indicators from road traffic pass-by noise |
spellingShingle |
Predicting vehicle category using psychoacoustic indicators from road traffic pass-by noise Barros, Ablenya Traffic noise Prediction Pshycoacoustics Engenharia e Tecnologia::Engenharia Civil Cidades e comunidades sustentáveis |
title_short |
Predicting vehicle category using psychoacoustic indicators from road traffic pass-by noise |
title_full |
Predicting vehicle category using psychoacoustic indicators from road traffic pass-by noise |
title_fullStr |
Predicting vehicle category using psychoacoustic indicators from road traffic pass-by noise |
title_full_unstemmed |
Predicting vehicle category using psychoacoustic indicators from road traffic pass-by noise |
title_sort |
Predicting vehicle category using psychoacoustic indicators from road traffic pass-by noise |
author |
Barros, Ablenya |
author_facet |
Barros, Ablenya Geluykens, Michiel Pereira, Frederico Goubert, Luc Freitas, E. F. Vuye, Cedric |
author_role |
author |
author2 |
Geluykens, Michiel Pereira, Frederico Goubert, Luc Freitas, E. F. Vuye, Cedric |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Barros, Ablenya Geluykens, Michiel Pereira, Frederico Goubert, Luc Freitas, E. F. Vuye, Cedric |
dc.subject.por.fl_str_mv |
Traffic noise Prediction Pshycoacoustics Engenharia e Tecnologia::Engenharia Civil Cidades e comunidades sustentáveis |
topic |
Traffic noise Prediction Pshycoacoustics Engenharia e Tecnologia::Engenharia Civil Cidades e comunidades sustentáveis |
description |
A set of road traffic pass-by noises containing more than 2000 vehicles was recorded following the Statistical Pass-By (SPB) methodology. Besides the acoustic descriptors, psychoacoustic indicators (loudness, sharpness, roughness, fluctuation strength) were retrieved for each pass-by of three vehicle categories defined in the standard (passenger cars, dual-axles and multi-axles heavy vehicles). A fourth vehicle category, comprised of delivery vans, was also investigated. All psychoacoustic indicators significantly differed among vehicle categories, meaning that not only intensity descriptors but also temporal and spectral features of pass-by noise distinguish those classes. With enough instances and a balanced dataset across groups, a machine-learning classification algorithm was trained with 70% of the dataset to predict vehicle categories using the psychoacoustic indicators. Classification accuracy on the test set reached 72%. Accuracy losses were primarily caused by 25% of the actual passenger cars being misclassified as vans and vice-versa. These results show the potential of using noise features other than uniquely the maximum noise level to classify vehicles in terms of noise perception. In this way, limiting classifications based on visual aspects of vehicle categories may be overcome, increasing the practicality and accuracy of measurements such as the SPB, as vehicle fleets worldwide are more consistently represented. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-03 2023-03-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/1822/88963 |
url |
https://hdl.handle.net/1822/88963 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Ablenya Barros, Michiel Geluykens, Frederico Pereira, Luc Goubert, Elisabete Freitas, Cedric Vuye; Predicting vehicle category using psychoacoustic indicators from road traffic pass-by noise. Proc. Mtgs. Acoust. 8 May 2023; 51 (1): 040001. https://doi.org/10.1121/2.0001775 1939-800X 10.1121/2.0001775 040001 https://pubs.aip.org/asa/poma/article/51/1/040001/2908153/Predicting-vehicle-category-using-psychoacoustic |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Acoustical Society of America (ASA) |
publisher.none.fl_str_mv |
Acoustical Society of America (ASA) |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799137761663385600 |