Deep Neural Networks for Road Surface Roughness Classification from Acoustic Signals
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L. Ambrosini, L. Gabrielli, F. Vesperini, S. Squartini, and L. Cattani, "Deep Neural Networks for Road Surface Roughness Classification from Acoustic Signals," Paper 9934, (2018 May.). doi:
L. Ambrosini, L. Gabrielli, F. Vesperini, S. Squartini, and L. Cattani, "Deep Neural Networks for Road Surface Roughness Classification from Acoustic Signals," Paper 9934, (2018 May.). doi:
Abstract: Vehicle noise emissions are highly dependent on the road surface roughness and materials. A classification of the road surface conditions may be useful in several regards, from driving assistance to in-car audio equalization. With the present work we exploit deep neural networks for the classification of the road surface roughness using microphones placed inside and outside the vehicle. A database is built to test our classification algorithms and results are reported, showing that the roughness classification is feasible with the proposed approach.
@article{ambrosini2018deep,
author={ambrosini, livio and gabrielli, leonardo and vesperini, fabio and squartini, stefano and cattani, luca},
journal={journal of the audio engineering society},
title={deep neural networks for road surface roughness classification from acoustic signals},
year={2018},
volume={},
number={},
pages={},
doi={},
month={may},}
@article{ambrosini2018deep,
author={ambrosini, livio and gabrielli, leonardo and vesperini, fabio and squartini, stefano and cattani, luca},
journal={journal of the audio engineering society},
title={deep neural networks for road surface roughness classification from acoustic signals},
year={2018},
volume={},
number={},
pages={},
doi={},
month={may},
abstract={vehicle noise emissions are highly dependent on the road surface roughness and materials. a classification of the road surface conditions may be useful in several regards, from driving assistance to in-car audio equalization. with the present work we exploit deep neural networks for the classification of the road surface roughness using microphones placed inside and outside the vehicle. a database is built to test our classification algorithms and results are reported, showing that the roughness classification is feasible with the proposed approach.},}
TY - paper
TI - Deep Neural Networks for Road Surface Roughness Classification from Acoustic Signals
SP -
EP -
AU - Ambrosini, Livio
AU - Gabrielli, Leonardo
AU - Vesperini, Fabio
AU - Squartini, Stefano
AU - Cattani, Luca
PY - 2018
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - May 2018
TY - paper
TI - Deep Neural Networks for Road Surface Roughness Classification from Acoustic Signals
SP -
EP -
AU - Ambrosini, Livio
AU - Gabrielli, Leonardo
AU - Vesperini, Fabio
AU - Squartini, Stefano
AU - Cattani, Luca
PY - 2018
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - May 2018
AB - Vehicle noise emissions are highly dependent on the road surface roughness and materials. A classification of the road surface conditions may be useful in several regards, from driving assistance to in-car audio equalization. With the present work we exploit deep neural networks for the classification of the road surface roughness using microphones placed inside and outside the vehicle. A database is built to test our classification algorithms and results are reported, showing that the roughness classification is feasible with the proposed approach.
Vehicle noise emissions are highly dependent on the road surface roughness and materials. A classification of the road surface conditions may be useful in several regards, from driving assistance to in-car audio equalization. With the present work we exploit deep neural networks for the classification of the road surface roughness using microphones placed inside and outside the vehicle. A database is built to test our classification algorithms and results are reported, showing that the roughness classification is feasible with the proposed approach.
Authors:
Ambrosini, Livio; Gabrielli, Leonardo; Vesperini, Fabio; Squartini, Stefano; Cattani, Luca
Affiliations:
Universita Politecnica delle Marche, Ancona, Italy; ASK Industries S.p.A., Montecavolo di Quattro Castella (RE), Italy(See document for exact affiliation information.)
AES Convention:
144 (May 2018)
Paper Number:
9934
Publication Date:
May 14, 2018Import into BibTeX
Subject:
Posters: Applications
Permalink:
http://www.aes.org/e-lib/browse.cfm?elib=19451