MATEC Web Conf.
Volume 308, 20202019 8th International Conference on Transportation and Traffic Engineering (ICTTE 2019)
|Number of page(s)||4|
|Section||Intelligent Transportation and System Design|
|Published online||12 February 2020|
Research on Traffic Acoustic Event Detection Algorithm Based on Sparse Autoencoder
1 Research Institute of Highway, Ministry of Transport, Beijing 100088, China
2 School of Communication Engineering, Nanjing Institute of Technology, Jiangsu Nanjing, 211167, China
a Corresponding author: email@example.com
Road traffic monitoring is very important for intelligent transportation. The detection of traffic state based on acoustic information is a new research direction. A vehicles acoustic event classification algorithm based on sparse autoencoder is proposed to analysis the traffic state. Firstly, the multidimensional Mel-cepstrum features and energy features are extracted to form a feature vector of 125 features; Secondly, based on the computed features, the five-layers autoencoder is trained. Finally, vehicle audio samples are collected and the trained autoencoder is tested. The experimental results show that detection rate of the traffic acoustic event reaches 94.9%, which is 12.3% higher than that of the traditional Convolutional Neural Networks (CNN) algorithm.
© The Authors, published by EDP Sciences, 2020
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