Zero crossing signature : a time-domain method applied to diesel and gasoline vehicle classification

Murovec, Jure (avtor), Prezelj, Jurij (avtor), Ćirić, Dejan (avtor), Milivojčević, Marko (avtor)

The Zero Crossing Signature (ZCS) approach is a novel time-domain feature extraction method that analyzes zero crossing points over multiple amplitude-shifted versions of an acoustic signal, enabling richer information extraction while maintaining computational efficiency. This method is especially suitable for real-time classification in the emerging Internet of Things (IOT) landscape, where resource-constrained devices require low-power solutions to support emission reduction efforts. In this study, the ZCS method was employed to showcase its full potential by classifying vehicles as diesel or gasoline based on their acoustic signatures. This classification task, applied to a database of car sounds acquired in the authors’ previous research, serves as a comprehensive demonstration of the method’s capabilities in distinguishing between engine types through characteristic sound wave patterns, highlighting its effectiveness and applicability in real-world scenarios. To further enhance feature extraction while keeping computational costs low, simple transformations using the first and second derivatives of the acoustic signals were applied, offering an efficient means of capturing additional signal characteristics. A dataset of 417 vehicle recordings was analyzed, and the classification performance of ZCS was compared with the conventional Zero Crossing (ZC) method using a Self-Organizing Map (SOM) configured with a 1D grid of 9 neurons. The study evaluated various time constants and crossing threshold densities for ZCS, benchmarking them against the classical ZC approach to assess their effectiveness.

• Jezik: Angleški jezik
• Ključne besede: zero crossing, time domain, vehicle classification, feature extraction, unsupervised learning, acoustic signal analysis
• 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: 2025
• Št. strani: Str. 5128-5138
• Številčenje: Vol. 25, iss. 3
• PID: 20.500.12556/RUL-167088
• UDK: 004:534
• ISSN pri članku: 1558-1748
• DOI: 10.1109/JSEN.2024.3516876
• COBISS.SI-ID: 221243907

Avtorske pravice

Podatek o licenci CC BY-NC-ND 4.0 je naveden na pristajalni strani članka. (Datum opombe: 7. 2. 2025)

• Datum objave v RUL: 07.02.2025

Gradivo je del revije

• Naslov: IEEE sensors journal
• Skrajšan naslov: IEEE sens. j.
• Založnik: IEEE Sensors Council
• ISSN: 1558-1748
• COBISS.SI-ID: 1542314

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.