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%.

翻译：道路车辆的检测与分类是交通监控中的关键任务。通常，计算机视觉算法主导着道路车辆分类任务，但该类方法在光线不足条件下可能性能下降，且需要较高的计算资源。此外，在敏感及安全区域安装摄像头会引发隐私问题。相比之下，声学交通监控具有成本效益高、在低光照条件及无法安装摄像头的区域能提供更高准确率的优势。本文研究声学车辆子类分类任务，将声学信号分为四类：轿车、卡车、自行车及无车辆。我们采用梅尔声谱图、MFCC和GFCC作为特征，通过数据预处理训练了一个简单且优化的CNN模型，该模型在该任务中表现优异。结合MFCC特征与精细数据预处理后，所提方法在IDMT交通数据集上以98.95%的准确率超越了现有最优基线方法。