  The detection and classification of vehicles on the road is a crucial task
for traffic monitoring. Usually, Computer Vision (CV) algorithms dominate the
task of vehicle classification on the road, but CV methodologies might suffer
in poor lighting conditions and require greater amounts of computational power.
Additionally, there is a privacy concern with installing cameras in sensitive
and secure areas. In contrast, acoustic traffic monitoring is cost-effective,
and can provide greater accuracy, particularly in low lighting conditions and
in places where cameras cannot be installed. In this paper, we consider the
task of acoustic vehicle sub-type classification, where we classify acoustic
signals into 4 classes: car, truck, bike, and no vehicle. We experimented with
Mel spectrograms, MFCC and GFCC as features and performed data pre-processing
to train a simple, well optimized CNN that performs well at the task. When used
with MFCC as features and careful data pre-processing, our proposed methodology
improves upon the established state-of-the-art baseline on the IDMT Traffic
dataset with an accuracy of 98.95%.
