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Analysis of unsupervised learning approach for classification of vehicle fuel type using psychoacoustic features
ID
Milivojčević, Marko
(
Avtor
),
ID
Ćirić, Dejan
(
Avtor
),
ID
Prezelj, Jurij
(
Avtor
),
ID
Murovec, Jure
(
Avtor
)
PDF - Predstavitvena datoteka,
prenos
(10,10 MB)
MD5: 5BD6F213EAAC6E2329F5AE744FE2D0C9
URL - Izvorni URL, za dostop obiščite
https://www.sciencedirect.com/science/article/pii/S0263224124002021
Galerija slik
Izvleček
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.
Jezik:
Angleški jezik
Ključne besede:
acoustic based acquisition system
,
acoustic analysis
,
internal combustion engines
,
self-organizing maps
,
psychoacoustic features
,
unsupervised classification
Vrsta gradiva:
Članek v reviji
Tipologija:
1.01 - Izvirni znanstveni članek
Organizacija:
FS - Fakulteta za strojništvo
Status publikacije:
Objavljeno
Različica publikacije:
Objavljena publikacija
Leto izida:
2024
Št. strani:
17 str.
Številčenje:
Vol. 227, art. 114318
PID:
20.500.12556/RUL-154503
UDK:
534:621.43
ISSN pri članku:
1873-412X
DOI:
10.1016/j.measurement.2024.114318
COBISS.SI-ID:
185908483
Datum objave v RUL:
19.02.2024
Število ogledov:
609
Število prenosov:
56
Metapodatki:
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Objavi na:
Gradivo je del revije
Naslov:
Measurement
Založnik:
Elsevier
ISSN:
1873-412X
COBISS.SI-ID:
23272709
Licence
Licenca:
CC BY-NC-ND 4.0, Creative Commons Priznanje avtorstva-Nekomercialno-Brez predelav 4.0 Mednarodna
Povezava:
http://creativecommons.org/licenses/by-nc-nd/4.0/deed.sl
Opis:
Najbolj omejujoča licenca Creative Commons. Uporabniki lahko prenesejo in delijo delo v nekomercialne namene in ga ne smejo uporabiti za nobene druge namene.
Sekundarni jezik
Jezik:
Slovenski jezik
Ključne besede:
sistem zajema na osnovi akustike
,
akustična analiza
,
motorji z notranjim izgorevanjem
,
psihoakustične značilke
,
samoorganizirajoče mreže
,
nenadzorovano učenje
Projekti
Financer:
ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
Številka projekta:
P2-0401
Naslov:
Energetsko strojništvo
Financer:
MESTD - Ministry of Education, Science and Technological Development of Republic of Serbia
Številka projekta:
451-03-47/2023-01/200102
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