Analysis of unsupervised learning approach for classification of vehicle fuel type using psychoacoustic features

Milivojčević, Marko (author), Čirić, Dejan (author), Prezelj, Jurij (author), Murovec, Jure (author)

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.

• Language: English
• Keywords: acoustic based acquisition system, acoustic analysis, internal combustion engines, self-organizing maps, psychoacoustic features, unsupervised classification
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FS - Faculty of Mechanical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2024
• Number of pages: 17 str.
• Numbering: Vol. 227, art. 114318
• PID: 20.500.12556/RUL-154503
• UDC: 534:621.43
• ISSN on article: 1873-412X
• DOI: 10.1016/j.measurement.2024.114318
• COBISS.SI-ID: 185908483
• Publication date in RUL: 19.02.2024

Record is a part of a journal

• Title: Measurement
• Publisher: Elsevier
• ISSN: 1873-412X
• COBISS.SI-ID: 23272709

Licences

• License: CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
• Link: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Description: The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.

Secondary language

• Language: Slovenian
• Keywords: sistem zajema na osnovi akustike, akustična analiza, motorji z notranjim izgorevanjem, psihoakustične značilke, samoorganizirajoče mreže, nenadzorovano učenje

Projects

• Funder: ARRS - Slovenian Research Agency
• Project number: P2-0401
• Name: Energetsko strojništvo

• Funder: MESTD - Ministry of Education, Science and Technological Development of Republic of Serbia
• Project number: 451- 03-47/2023-01/200102