20.500.12556/RUL-169530 Assessing air and noise pollution through acoustic classification of vehicles fuel types using deep learning Measuring traffic emissions typically requires expensive analyzers, limiting the scalability of monitoring systems. In this study, we present a novel, cost-effective method for assessing noise and air pollution by classifying vehicles based on their acoustic signatures using artificial intelligence. We collected a dataset of 449 sound recordings of vehicles in an idle state within a real-world urban environment to minimize background noise and vehicle related variables. Psycho-acoustic features-loudness, sharpness, roughness, fluctuation strength, and tonality-and features derived from the Hilbert-Huang Transform (HHT) were extracted from the signals to capture the unique acoustic signatures of different engine types. Using these features, we developed a deep neural network (DNN) capable of distinguishing among petrol, diesel, and high-emission diesel vehicles with an accuracy of 92%. This approach demonstrates that acoustic emissions, i.e., traffic noise, can be linked to exhaust gas emissions. Our findings confirm that acoustic analysis provides an alternative to traditional methods for monitoring urban air quality. By enabling large-scale and dense sensor networks, this methodology offers substantial benefits for real-time environmental monitoring, urban planning, and regulatory enforcement. Additionally, the dual focus on air and noise pollution supports more comprehensive assessments of urban environmental impacts. This study highlights the potential of integrating advanced acoustic analysis with machine learning to create accessible, non-intrusive tools for pollution monitoring and mitigation. acoustic vehicle classification psychoacoustics Hilbert-Huang transformation noise pollution air pollution environmental acoustic analysis engine fuel type akustična klasifikacija vozil psihoakustika Hilbert-Huangova transformacija onesnaženost s hrupom onesnaženost zraka okoljska akustična analiza vrsta goriva za motor true false true Angleški jezik Slovenski jezik Članek v reviji 2025-06-02 12:35:12 2025-06-02 12:35:45 2025-06-04 04:01:12 0000-00-00 00:00:00 2025 0 0 15 str. no. 10, art. e43426 Vol. 11 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 534 2405-8440 10.1016/j.heliyon.2025.e43426 237944067 RAZ_Hvastja_Andrej_2025.pdf RAZ_Hvastja_Andrej_2025.pdf 1 271096A0CE9968719B0C023B789A8DB0 01e632c6bc8ec72f3ec327af215c15e82018ed3eec5a00d000b6e5e8ddc38b37 20f4bafc-3f9d-11f0-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=210969 https://www.sciencedirect.com/science/article/pii/S2405844025018110 1 0ab83e35-3f9c-11f0-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=210967 Fakulteta za strojništvo 0 0 0