Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Network-based Acoustic Vehicle Counting   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.

35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an
6
The categories of this paper are: astro-ph
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
20
The categories of this paper are: acc-phys
28
The categories of this paper are: alg-geom
16
The categories of this paper are: stat
18
The categories of this paper are: econ
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int