| Title |
Sound-Convolutional Recurrent Neural Networks for Vehicle Classification Based on Vehicle Acoustic Signals |
| ID_Doc |
42630 |
| Authors |
Luo, YX; Chen, LG; Wu, Q; Zhang, XH |
| Title |
Sound-Convolutional Recurrent Neural Networks for Vehicle Classification Based on Vehicle Acoustic Signals |
| Year |
2021 |
| Published |
|
| DOI |
10.1109/ICSCGE53744.2021.9654357 |
| Abstract |
Vehicle classification based on acoustic signals in urban environment provides valuable perception information for smart city management. In order to improve the accuracy of current vehicle sound classification, we propose a Sound-Convolutional Recurrent Neural Networks (S-CRNN) method. It combines convolutional neural networks (CNN) and recurrent neural network (RNN). By comparing the weighted F1 score (F1) and error rate (ER), it is proved that the proposed S-CRNN method has better classification performance than the original Sound-Convolutional Neural Networks (S-CNN) method, especially in the vehicles level, the weighted F1 value increases to 28.5 %. Long short-term memory (LSTM) and Gate Recurrent Unit (GRU) are both used as RNN for comparison. And the S-CRNN model with GRU reduces the training time by 2.65 hours, maintaining the main performance in the meantime. |
| Author Keywords |
convolutional recurrent neural network; vehicles classification; neutral networks; acoustic signal processing |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000850131200020 |
| WoS Category |
Green & Sustainable Science & Technology; Energy & Fuels |
| Research Area |
Science & Technology - Other Topics; Energy & Fuels |
| PDF |
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