Deep Learning-Based Road Traffic Noise Annoyance Assessment

Jie Wang, Xuejian Wang, Minmin Yuan (), Wenlin Hu, Xuhong Hu and Kexin Lu
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Jie Wang: School of Electronics and Communication Engineering, Guangzhou University, Guangzhou 510006, China
Xuejian Wang: School of Electronics and Communication Engineering, Guangzhou University, Guangzhou 510006, China
Minmin Yuan: Research Institute of Highway Ministry of Transport, Beijing 100088, China
Wenlin Hu: National Engineering Laboratory for Digital Construction and Evaluation of Urban Rail Transit, China Railway Design Corporation, Tianjin 300162, China
Xuhong Hu: National Engineering Laboratory for Digital Construction and Evaluation of Urban Rail Transit, China Railway Design Corporation, Tianjin 300162, China
Kexin Lu: Research Institute of Highway Ministry of Transport, Beijing 100088, China

IJERPH, 2023, vol. 20, issue 6, 1-16

Abstract: 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.

Keywords: traffic noise annoyance; deep learning; transfer learning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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