An evaluation of feature extraction methods for vehicle classification based on acoustic signals

Classification of ground vehicles based on acoustic signals can be employed effectively in battlefield surveillance, traffic control, and many other applications. The classification performance depends on the selection of signal features that determine the separation of different signal classes. In this paper, we investigate two feature extraction methods for acoustic signals from moving ground vehicles. The first one is based on spectrum distribution and the second one on wavelet packet transform. These two methods are evaluated using metrics such as separability ratio and the correct classification rate. The correct classification rate not only depends on the feature extraction method but also on the type of the classifier. This drives us to evaluate the performance of different classifiers, such K-nearest neighbor algorithm (KNN), and support vector machine (SVM). It is found that, for vehicle sound data, a discrete spectrum based feature extraction method outperforms wavelet packet transform method. Experimental results verify that support vector machine is an efficient classifier for vehicles using acoustic signals.