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

Povzetek

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.

Ključne besede

sistem zajema na osnovi akustike;akustična analiza;motorji z notranjim izgorevanjem;psihoakustične značilke;samoorganizirajoče mreže;nenadzorovano učenje;acoustic based acquisition system;acoustic analysis;internal combustion engines;self-organizing maps;psychoacoustic features;unsupervised classification;

Podatki

Jezik: Angleški jezik
Leto izida:
Tipologija: 1.01 - Izvirni znanstveni članek
Organizacija: UL FS - Fakulteta za strojništvo
UDK: 534:621.43
COBISS: 185908483 Povezava se bo odprla v novem oknu
ISSN: 1873-412X
Št. ogledov: 276
Št. prenosov: 22
Ocena: 0 (0 glasov)
Metapodatki: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Ostali podatki

Sekundarni jezik: Slovenski jezik
Sekundarne ključne besede: sistem zajema na osnovi akustike;akustična analiza;motorji z notranjim izgorevanjem;psihoakustične značilke;samoorganizirajoče mreže;nenadzorovano učenje;
Vrsta dela (COBISS): Članek v reviji
Strani: str. 1-17
Zvezek: ǂVol. ǂ227, [article no.] 114318
Čas izdaje: Mar. 2024
DOI: 10.1016/j.measurement.2024.114318
ID: 22947216