The focus of this paper is on classification of different vehicles using sound emanated from the vehicles. In this paper, quadratic discriminant analysis classifies audio signals of passing vehicles to bus, car, motor, and truck categories based on features such as short time energy, average zero cross rate, and pitch frequency of periodic segments of signals. Simulation results show that just by considering high energy feature vectors, better classification accuracy can be achieved due to the correspondence of low energy regions with noises of the background. To separate these elements, short time energy and average zero cross rate are used simultaneously. In our method, we have used a few features which are easy to be calculated in time domain and enable practical implementation of efficient classifier. Although, the computation complexity is 2 low, the classification accuracy is comparable with other classification methods based on long feature vectors reported in literature for this problem.
Feature vector included, among others, short time energy, average zero crossing rate, and pitch frequency of periodic segments of signals, yielding 80% correctness for SVM with 12 Mel coefficients REF .
Classification of Vehicles Based on Audio Signals using Quadratic Discriminant Analysis and High Energy Feature Vectors
International Journal on Soft Computing (IJSC) Vol.6, No. 1, February 2015
Computer Science Engineering