Neural Network-based Acoustic Vehicle Counting
Abstract
This paper addresses acoustic vehicle counting using one-channel audio. We predict the pass-by instants of vehicles from local minima of clipped vehicle-to-microphone distance. This distance is predicted from audio using a two-stage (coarse-fine) regression, with both stages realised via neural networks (NNs). Experiments show that the NN-based distance regression outperforms by far the previously proposed support vector regression. The confidence interval for the mean of vehicle counting error is within . Besides the minima-based counting, we propose a deep learning counting that operates on the predicted distance without detecting local minima. Although outperformed in accuracy by the former approach, deep counting has a significant advantage in that it does not depend on minima detection parameters. Results also show that removing low frequencies in features improves the counting performance.
Cite
@article{arxiv.2010.11659,
title = {Neural Network-based Acoustic Vehicle Counting},
author = {Slobodan Djukanović and Yash Patel and Jiři Matas and Tuomas Virtanen},
journal= {arXiv preprint arXiv:2010.11659},
year = {2021}
}