Generalization Ability of a Support Vector Classifier Applied to Vehicle Data in a Microphone Network

Abstract

Audio recordings of vehicles passing a microphone network are studied with respect to the classification ability under different weather and local conditions. The audio data base includes recordings in different seasons, recordings at various sensor locations and also recordings using different microphones. A support vector machine (SVM) is used to classify vehicles from normalized, low-frequency spectral features of short time chunks of the audio signals. The classification performance using individual time chunks is estimated, as well as the accuracy of fusing data from the different microphones in the network. The study shows that, combining temporal and spatial data, a vehicle traversing a microphone network can be correctly classified in up to 90 percent of all runs. A more demanding test, classifying data from a totally independent measurement equipment, yields 70 percent correct classifications