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Beyond noise levels: vehicle classification using psychoacoustic indicators from pass-by road traffic noise and their correlations with speed and temperature

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Resumo:Environmental noise control regulations typically employ noise level descriptors to set limits for noise exposure. However, other characteristics of noise, such as frequency content, temporal patterns and masking, have been proven to influence the perception of acoustic environments. In this sense, psychoacoustic indicators offer an objective means of establishing relationships between physical characteristics of noise and the human auditory sensation phenomena. This study explored psychoacoustic indicators of pass-by vehicle noise across different vehicle categories, driving speeds, and temperatures. Moreover, the indicators were exploited as features to train a classification algorithm to predict vehicle category. Over 2000 vehicle noise samples were collected using the Statistical Pass-By (SPB) method, categorized into three classes according to ISO 11819-1, besides an additional class for delivery vans. Correction coefficients were obtained for temperature and speed to noise levels, loudness, roughness, sharpness and fluctuation strength. The differences in these indicators based on vehicle category were then discussed. A vehicle-category predictive model using the three vehicle categories defined in ISO 11819-1 yielded 84% accuracy. Including vans as an extra vehicle category dropped accuracy to 72% due to their misclassification with passenger cars. Combining these two categories increased overall accuracy to 86%. These findings could enable a less visual-dependent vehicle categorization so that vehicle fleets worldwide are more consistently classified in terms of noise. Additionally, psychoacoustic indicators appear to be valuable features for vehicle classification systems aimed to resemble the human auditory experience.
Autores principais:Barros, Ablenya
Outros Autores:Geluykens, Michiel; Pereira, Frederico; Freitas, E. F.; Faria, Susana; Goubert, Luc; Vuye, Cedric
Assunto:Audio signal classification Logistic regression Machine learning Statistical pass-by
Ano:2023
País:Portugal
Tipo de documento:artigo
Tipo de acesso:acesso restrito
Instituição associada:Universidade do Minho
Idioma:inglês
Origem:RepositóriUM - Universidade do Minho

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