Acoustic Signal Classification of Field Vehicles Based on Combination of LSTM and CNN [结合LSTM与CNN的野外车辆声信号分类]

作者：Li X.; Wang Y.; Li B.

来源：Piezoelectrics & Acoustooptics, 2021, 43(3): 379-384.

DOI：10.11977/j.issn.1004-2474.2021.03.018

• Channel attention mechanism
• Convolutional neural network(CNN)
• Feature fusion
• Long short-term memory modal(LSTM)
• Parallel multi-scale feature extraction
• Vehicle acoustic signal classification

In order to solve the problem of low classification performance and serious influence of wind noise on the acoustic signals of small wheeled vehicles, large wheeled vehicles and tracked vehicles collected by micro acoustic sensors in the field, a classification algorithm network for field vehicle classification (FVNet) based on the combination of long short-term memory modal(LSTM) and the multi-scale and multi-level feature fusion convolutional neural network(CNN) is proposed. Firstly, an one-layer LSTM network is used to extract the temporal features of the acoustic signal, which makes full use of the long-term dependence of the acoustic signal. Then the CNN is used to extract multi-scale features in parallel to avoid the loss of features in the process of network deepening. The channel attention mechanism is introduced to fuse multi-scale and multi-level features to enhance the multi-scale and multi-level key feature information. Finally, it is verified on the same data set. The experimental results show that the total recognition rate of FVNet algorithm for three types of vehicles can reach 94.95%, which is 14.61% higher than that of traditional methods, and achieves better classification effect.

• 单位
  中国科学院大学