Sound-Convolutional Recurrent Neural Networks for Vehicle Classification Based on Vehicle Acoustic Signals

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.