Neural network-based acoustic vehicle counting
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00351751" target="_blank" >RIV/68407700:21230/21:00351751 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.23919/EUSIPCO54536.2021.9615925" target="_blank" >https://doi.org/10.23919/EUSIPCO54536.2021.9615925</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.23919/EUSIPCO54536.2021.9615925" target="_blank" >10.23919/EUSIPCO54536.2021.9615925</a>
Alternative languages
Result language
angličtina
Original language name
Neural network-based acoustic vehicle counting
Original language description
This paper addresses acoustic vehicle counting usingone-channel audio. We predict the pass-by instants of vehiclesfrom 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 regressionoutperforms by far the previously proposed support vectorregression. The95%confidence interval for the mean of vehiclecounting error is within[0.28%,-0.55%]. Besides the minima-based counting, we propose a deep learning counting that op-erates 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 notdepend on minima detection parameters. Results also show thatremoving low frequencies in features improves the countingperformance.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
29th European Signal Processing Conference (EUSIPCO)
ISBN
9789082797060
ISSN
2219-5491
e-ISSN
2076-1465
Number of pages
5
Pages from-to
561-565
Publisher name
IEEE Signal Processing Society
Place of publication
New Jersey
Event location
Dublin (virtual)
Event date
Aug 23, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000764066600111