Publication
Title
Predicting vehicle category using psychoacoustic indicators from road traffic pass-by noise
Author
Abstract
A set of road traffic pass-by noises containing more than 2000 vehicles was recorded following the Statistical Pass-By (SPB) methodology. Besides the acoustic descriptors, psychoacoustic indicators (loudness, sharpness, roughness, fluctuation strength) were retrieved for each pass-by of three vehicle categories defined in the standard (passenger cars, dual-axles and multi-axles heavy vehicles). A fourth vehicle category, comprised of delivery vans, was also investigated. All psychoacoustic indicators significantly differed among vehicle categories, meaning that not only intensity descriptors but also temporal and spectral features of pass-by noise distinguish those classes. With enough instances and a balanced dataset across groups, a machine-learning classification algorithm was trained with 70% of the dataset to predict vehicle categories using the psychoacoustic indicators. Classification accuracy on the test set reached 72%. Accuracy losses were primarily caused by 25% of the actual passenger cars being misclassified as vans and vice-versa. These results show the potential of using noise features other than uniquely the maximum noise level to classify vehicles in terms of noise perception. In this way, limiting classifications based on visual aspects of vehicle categories may be overcome, increasing the practicality and accuracy of measurements such as the SPB, as vehicle fleets worldwide are more consistently represented.
Language
English
Source (journal)
Proceedings of meetings on acoustics
Source (book)
184th Meeting of the Acoustical Society of America, 8–12 May, 2023, Chicago, Illinois, USA
Publication
Acoustical Society of America , 2023
DOI
10.1121/2.0001775
Volume/pages
51 :1 (2023) , p. 1-9
Article Reference
040001
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
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Research group
Publication type
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Affiliation
Publications with a UAntwerp address
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Creation 12.12.2023
Last edited 13.12.2023
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