  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 $
95\% $ confidence interval for the mean of vehicle counting error is within
$[0.28\%, -0.55\%]$. 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.
