  The paper presents a method for audio-based vehicle counting (VC) in
low-to-moderate traffic using one-channel sound. We formulate VC as a
regression problem, i.e., we predict the distance between a vehicle and the
microphone. Minima of the proposed distance function correspond to vehicles
passing by the microphone. VC is carried out via local minima detection in the
predicted distance. We propose to set the minima detection threshold at a point
where the probabilities of false positives and false negatives coincide so they
statistically cancel each other in total vehicle number. The method is trained
and tested on a traffic-monitoring dataset comprising $422$ short, $20$-second
one-channel sound files with a total of $ 1421 $ vehicles passing by the
microphone. Relative VC error in a traffic location not used in the training is
below $ 2 \%$ within a wide range of detection threshold values. Experimental
results show that the regression accuracy in noisy environments is improved by
introducing a novel high-frequency power feature.

Sound;Machine Learning;Audio and Speech Processing