Artificial neural network based detectability prediction of synthesized exterior electric vehicle sounds
Research output: Contribution to conferences › Paper › Contributed › peer-review
Contributors
Abstract
The safety for traffic participants like pedestrians and cyclists might be effected by the current electrification of vehicles. Detectability of electric vehicle sounds is an important attribute for safety reasons. The aim of this work is to determine the detectability of different electric vehicle sounds for a constant speed, single car pass-by situation. For this purpose, the differences in detection time are investigated with perception studies. The correlation between physical-psychoacoustical parameters and detection time estimations obtained from jury testing is also investigated in this study. Moreover, an artificial neural network (ANN) is also used as a prediction tool of detectability estimations for further evaluations of different possible stimuli. Lastly, advantages and shortcomings of using ANNs for detectability estimations are also discussed.
Details
Original language | English |
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Publication status | Published - 2017 |
Peer-reviewed | Yes |
Conference
Title | 46th International Congress and Exposition on Noise Control Engineering |
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Subtitle | Taming Noise and Moving Quiet |
Abbreviated title | INTER-NOISE 2017 |
Conference number | 46 |
Duration | 27 - 30 August 2017 |
City | Hong Kong |
Country | China |
External IDs
ORCID | /0000-0002-0803-8818/work/142257113 |
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Keywords
ASJC Scopus subject areas
Keywords
- Detectability, Electric vehicle, Neural networks, Psychoacoustics, Safety, Sound quality