A FIELD TRIAL OF ACOUSTIC SIGNATURE ANALYSIS FOR VEHICLE CLASSIFICATION

The aim of this research is to investigate the feasibility of developi ng a traffic monitoring detector for the purpose of reliable on-line v ehicle classification to aid traffic management systems. The detector used was a directional microphone connected to a DAT (Digital Audio Ta pe) recorder. The digital signal was pre-processed by LPC (Linear Pred ictive Coding) parameter conversion based on autocorrelation analysis. A Time Delay Neural Network (TDNN) was chosen to classify individual travelling vehicles based on their speed-independent acoustic signatur e. Locations for data acquisition included roadside recordings at a nu mber of two-way urban road sites in the city of Leeds with no control over the environmental parameters such as background noise, interferen ce from other travelling vehicles or the speed of the recorded vehicle s. The results and performance analysis of TDNN vehicle classification , the convergence for training patterns and accuracy of test patterns are fully illustrated. The paper also provides a description of the TD NN architecture and training algorithm, and an overview of the LPC pre -processing and feature extraction technique as applied to audio monit oring of road traffic. In the final phase of the experiment, the four broad categorisations of vehicles for training the network consisted o f: buses or lorries; small or large saloons; various types of motorcyc les; and light goods vehicles or vans. A TDNN network was successfully trained with 94% accuracy for the training patterns and 82.4% accurac y for the test patterns. (C) 1997 Elsevier Science Ltd. All rights res erved.