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  • Analysis of unsupervised learning approach for classification of vehicle fuel type using psychoacoustic features [Elektronski vir]
    Milivojčević, Marko ...
    Much research has been done in the field of classifying vehicles based on their fuel type. One of the many potential applications is to improve the quality of life in urban areas by separating ... vehicles based on their pollution level. Real-time classification and implementation of appropriate on-site IoT measurement devices is critical to developing a system that accurately identifies vehicle's fuel type without violating driver privacy. In this paper, a classification system based on psychoacoustic features extracted from sound recordings is investigated. Unsupervised learning was implemented as it is able to detect hidden connections within the input space without relying on labelling data. Our goal was to develop and explore a relatively fast classification system, focusing on a short acquisition time. A self-organizing map with a 10-dimensional input space was used and effective classification with five single-valued features was demonstrated. The analysis of the input space shows the difficulty and complexity of such an approach and leaves room for further improvement.
    Source: Measurement [Elektronski vir]. - ISSN 1873-412X (Vol. 227, [article no.] 114318, Mar. 2024, str. 1-17)
    Type of material - e-article ; adult, serious
    Publish date - 2024
    Language - english
    COBISS.SI-ID - 185908483

    Link(s):

    https://www.sciencedirect.com/science/article/pii/S0263224124002021
    Repository of the University of Ljubljana – RUL
    DOI

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