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首页> 外文会议>2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications >Performance Analysis of SVM, ANN and KNN Methods for Acoustic Road-Type Classification
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Performance Analysis of SVM, ANN and KNN Methods for Acoustic Road-Type Classification

机译：SVM，ANN和KNN方法在道路分类中的性能分析

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摘要

In the study, a low-cost acoustic system which classifies different roads using acoustic signal processing tool is proposed (group1 road types: asphalt, gravel, stony and snowy road; group2 road types: asphalt data with car pass noise, asphalt data with rain noise, asphalt data with tire squeal noise). Thus it is aimed to estimate road/tire friction forces using slip ratio/friction curve in the active safety systems of the automobiles. Because friction forces cannot be measured directly and it can be only observed or estimated. In the study, acoustic data features which are linear predictive coding (LPC), power spectrum coefficients (PSC) and mel-frequency cepstrum coefficients (MFCC) are used for the acoustic signal processing methods with minimum variance and maximum distance principle. The features are extracted using time windows 0.1 second as the best representative window of signal properties. The classification process is also executed by support vector machine (SVM), artificial neural network (ANN), K-nearest neighbors (KNN) algorithms and compared to different road types. The most important difference of this study from our previous studies is that it compares performances of these three classification methods for different feature vectors obtained from different road conditions and indicates that the KNN is better method than SVM and ANN methods for the acoustic road type classification. According to the results, the KNN method classifies group1 road data with %90 accuracy rate and group2 road data with % 100 accuracy rate.
机译：在研究中，提出了一种使用声学信号处理工具对不同道路进行分类的低成本声学系统（第1组道路类型：柏油路，碎石路，石质路和雪道；第2组道路类型：带车通过噪声的沥青数据，带雨水的沥青数据噪音，带有轮胎尖叫声的沥青数据）。因此，目的是在汽车的主动安全系统中使用滑移率/摩擦曲线来估计道路/轮胎摩擦力。因为摩擦力不能直接测量，只能观察或估计。在研究中，采用线性预测编码（LPC），功率谱系数（PSC）和梅尔频率倒谱系数（MFCC）的声学数据特征用于具有最小方差和最大距离原理的声学信号处理方法。使用0.1秒的时间窗口作为信号属性的最佳代表窗口来提取特征。分类过程也由支持向量机（SVM），人工神经网络（ANN），K近邻（KNN）算法执行，并与不同的道路类型进行比较。这项研究与我们先前的研究最重要的区别在于，它比较了这三种分类方法对从不同道路条件获得的不同特征向量的性能，并表明对于声路类型分类，KNN比SVM和ANN方法更好。根据结果​​，KNN方法将准确率为％90的第1组道路数据和准确率为％100的第2组道路数据分类。

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