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Research on Vehicle Classification and Recognition Method Based on Vehicle Acoustic Signal CNN Analysis

机译：基于车辆声学信号CNN分析的车辆分类和识别方法研究

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摘要

The present "shallow classification model" have shortcomings on modeling and representation ability, feature extraction, classification performance and so on. This study aims to improve the typical LeNet-5 convolution neural network and obtain three kinds of CNN structures to realize the classification of large and small vehicles. Firstly, we extracted the MFCC feature of vehicle acoustic signals; then took the feature signals as training samples; lastly adjusted the study rate, convolution kernel size and quantity in accordance with experiment and obtained the results. The experimental results indicate that the improved CNN model is better than the traditional machine learning method; and the classification performance of the improved CNN model is improved with the increase of data volume, and the accuracy of the test samples is 96.8%.
机译：本“浅层分类模型”具有对建模和呈现能力的缺点，功能提取，分类性能等。本研究旨在改善典型的LENET-5卷积神经网络，并获得三种CNN结构，以实现大型车辆的分类。首先，我们提取了车辆声信号的MFCC特征;然后将特征信号作为培训样本拿走;最后根据实验调整了研究速率，卷积内核大小和数量，并获得了结果。实验结果表明，改进的CNN模型优于传统的机器学习方法;随着数据量的增加，改进的CNN模型的分类性能提高，测试样品的准确性为96.8％。

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