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An Approach of Passive Vehicle Type Recognition by Acoustic Signal Based on SVM

机译:基于SVM的声学信号的被动车辆类型识别方法

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An approach of power spectrum estimation is utilized to extract the feature vectors from acoustic signal radiated from different types of moving vehicles. A method of feature selection based on principal component analysis (PCA) is proposed to reconstruct effective feature vectors via dimension reduction. The classification of three typical targets is achieved by supported vector machine (SVM). Experiment results show that the approach presented in the paper for automatic recognition of vehicle type is effective.
机译:利用功率谱估计的方法来从不同类型的移动车辆辐射的声信号中提取特征向量。提出了一种基于主成分分析(PCA)的特征选择方法,通过减小来重建有效特征向量。三种典型目标的分类是通过支持的向量机(SVM)实现的。实验结果表明,自动识别车辆类型纸张中提出的方法是有效的。

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