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Deep Learning-Based Road Traffic Noise Annoyance Assessment

With the development of urban road traffic, road noise pollution is becoming a public concern. Controlling and reducing the harm caused by traffic noise pollution have been the hot spots of traffic noise management research. The subjective annoyance level of traffic noise has become one of the most...

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Detalles Bibliográficos
Autores principales: Wang, Jie, Wang, Xuejian, Yuan, Minmin, Hu, Wenlin, Hu, Xuhong, Lu, Kexin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10049706/
https://www.ncbi.nlm.nih.gov/pubmed/36982107
http://dx.doi.org/10.3390/ijerph20065199
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author Wang, Jie
Wang, Xuejian
Yuan, Minmin
Hu, Wenlin
Hu, Xuhong
Lu, Kexin
author_facet Wang, Jie
Wang, Xuejian
Yuan, Minmin
Hu, Wenlin
Hu, Xuhong
Lu, Kexin
author_sort Wang, Jie
collection PubMed
description With the development of urban road traffic, road noise pollution is becoming a public concern. Controlling and reducing the harm caused by traffic noise pollution have been the hot spots of traffic noise management research. The subjective annoyance level of traffic noise has become one of the most important measurements for evaluating road traffic pollution. There are subjective experimental methods and objective prediction methods to assess the annoyance level of traffic noise: the subjective experimental method usually uses social surveys or listening experiments in laboratories to directly assess the subjective annoyance level, which is highly reliable, but often requires a lot of time and effort. The objective method extracts acoustic features and predicts the annoyance level through model mapping. Combining the above two methods, this paper proposes a deep learning model-based objective annoyance evaluation method, which directly constructs the mapping between the noise and annoyance level based on the listening experimental results and realizes the rapid evaluation of the noise annoyance level. The experimental results show that this method has reduced the mean absolute error by 30% more than the regression algorithm and neural network, while its performance is insufficient in the annoyance interval where samples are lacking. To solve this problem, the algorithm adopts transfer learning to further improve the robustness with a 30% mean absolute error reduction and a 5% improvement in the correlation coefficient between the true results and predicted results. Although the model trained on college students’ data has some limitations, it is still a useful attempt to apply deep learning to noise assessment.
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spelling pubmed-100497062023-03-29 Deep Learning-Based Road Traffic Noise Annoyance Assessment Wang, Jie Wang, Xuejian Yuan, Minmin Hu, Wenlin Hu, Xuhong Lu, Kexin Int J Environ Res Public Health Article With the development of urban road traffic, road noise pollution is becoming a public concern. Controlling and reducing the harm caused by traffic noise pollution have been the hot spots of traffic noise management research. The subjective annoyance level of traffic noise has become one of the most important measurements for evaluating road traffic pollution. There are subjective experimental methods and objective prediction methods to assess the annoyance level of traffic noise: the subjective experimental method usually uses social surveys or listening experiments in laboratories to directly assess the subjective annoyance level, which is highly reliable, but often requires a lot of time and effort. The objective method extracts acoustic features and predicts the annoyance level through model mapping. Combining the above two methods, this paper proposes a deep learning model-based objective annoyance evaluation method, which directly constructs the mapping between the noise and annoyance level based on the listening experimental results and realizes the rapid evaluation of the noise annoyance level. The experimental results show that this method has reduced the mean absolute error by 30% more than the regression algorithm and neural network, while its performance is insufficient in the annoyance interval where samples are lacking. To solve this problem, the algorithm adopts transfer learning to further improve the robustness with a 30% mean absolute error reduction and a 5% improvement in the correlation coefficient between the true results and predicted results. Although the model trained on college students’ data has some limitations, it is still a useful attempt to apply deep learning to noise assessment. MDPI 2023-03-15 /pmc/articles/PMC10049706/ /pubmed/36982107 http://dx.doi.org/10.3390/ijerph20065199 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wang, Jie
Wang, Xuejian
Yuan, Minmin
Hu, Wenlin
Hu, Xuhong
Lu, Kexin
Deep Learning-Based Road Traffic Noise Annoyance Assessment
title Deep Learning-Based Road Traffic Noise Annoyance Assessment
title_full Deep Learning-Based Road Traffic Noise Annoyance Assessment
title_fullStr Deep Learning-Based Road Traffic Noise Annoyance Assessment
title_full_unstemmed Deep Learning-Based Road Traffic Noise Annoyance Assessment
title_short Deep Learning-Based Road Traffic Noise Annoyance Assessment
title_sort deep learning-based road traffic noise annoyance assessment
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10049706/
https://www.ncbi.nlm.nih.gov/pubmed/36982107
http://dx.doi.org/10.3390/ijerph20065199
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