Exploring Sound Signature for Vehicle Detection and Classification Using ANN

Jobin George, Anila Cyril, Bino I. Koshy and Leena Mary,Rajiv Gandhi Institute of Technology, India

ABSTRACT

This paper attempts to explore the possibility of using sound signatures for vehicle detection and classification purposes. Sound emitted by vehicles are captured for a two lane undivided road carrying moderate traffic. Simultaneous arrival of different types vehicles, overtaking at the study location, sound of horns, random but identifiable back ground noises, continuous high energy noises on the back ground are the different challenges encountered in the data collection. Different features were explored out of which smoothed log energy was found to be useful for automatic vehicle detection by locating peaks. Melfrequency ceptral coefficients extracted from fixed regions around the detected peaks along with the manual vehicle labels are utilised to train an Artificial Neural Network (ANN). The classifier for four broad classes heavy, medium, light and horns was trained. The ANN classifier developed was able to predict categories well.

KEYWORDS

Traffic characterises, vehicle detection, multilayer feedforward neural network, vehicle classification. 

Original Source URL: https://airccse.org/journal/ijsc/papers/4213ijsc03.pdf

https://airccse.org/journal/ijsc/current2013.html

EXPLORING SOUND SIGNATURE FOR VEHICLE DETECTION AND CLASSIFICATION USING ANN

EXPLORING SOUND SIGNATURE FOR VEHICLE DETECTION AND CLASSIFICATION USING ANN 

Jobin George1 , Anila Cyril2 , Bino I. Koshy3 and Leena Mary4 1Department of EC Engineering, Rajiv Gandhi Institute of Technology, Kottayam, India jobing4@gmail.com 2,3 Department of Civil Engineering, Rajiv Gandhi Institute of Technology, Kottayam, India anilacyril110@gmail.com, bino@rit.ac.in 4Department of CS and Engineering, Rajiv Gandhi Institute of Technology, Kottayam, India leena.mary@rit.ac.in 

ABSTRACT 

This paper attempts to explore the possibility of using sound signatures for vehicle detection and classification purposes. Sound emitted by vehicles are captured for a two lane undivided road carrying moderate traffic. Simultaneous arrival of different types vehicles, overtaking at the study location, sound of horns, random but identifiable back ground noises, continuous high energy noises on the back ground are the different challenges encountered in the data collection. Different features were explored out of which smoothed log energy was found to be useful for automatic vehicle detection by locating peaks. Melfrequency ceptral coefficients extracted from fixed regions around the detected peaks along with the manual vehicle labels are utilised to train an Artificial Neural Network (ANN). The classifier for four broad classes heavy, medium, light and horns was trained. The ANN classifier developed was able to predict categories well. 

KEYWORDS 

Traffic characterises, vehicle detection, multilayer feedforward neural network, vehicle classification.  

Original Source URL : http://airccse.org/journal/ijsc/papers/4213ijsc03.pdf

http://airccse.org/journal/ijsc/current2013.html