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.

Deep Neural Networks for Road Surface Roughness Classification from Acoustic Signals / Ambrosini, Livio; Gabrielli, Leonardo; Vesperini, Fabio; Squartini, Stefano; Cattani, Luca. - ELETTRONICO. - (2018). (Intervento presentato al convegno Audio Engineering Society Convention 144 tenutosi a Milan, Italy nel May 2018).

Deep Neural Networks for Road Surface Roughness Classification from Acoustic Signals

AMBROSINI, LIVIO;Gabrielli, Leonardo;Vesperini, Fabio;Squartini, Stefano;
2018-01-01

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.
2018
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/258786
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