Classification of Vehicles Based on Audio Signals using Quadratic Discriminant Analysis and High Energy Feature Vectors

TL;DR

This paper presents a vehicle classification method using audio signals and quadratic discriminant analysis, focusing on high energy features like short time energy, zero cross rate, and pitch frequency for improved accuracy and practical implementation.

Contribution

The paper introduces a vehicle classification approach based on simple, time-domain features and quadratic discriminant analysis, achieving comparable accuracy with low computational complexity.

Findings

High energy feature vectors improve classification accuracy.

Simultaneous use of short time energy and zero cross rate enhances noise separation.

Method achieves accuracy comparable to more complex approaches.

Abstract

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 low, the classification accuracy is comparable with other classification methods based on long feature vectors reported in literature for this problem.