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
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