Zero Crossing Signature: A Time-Domain Method Applied to Diesel and Gasoline Vehicle Classification

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...

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Bibliographic Details
Published in:IEEE sensors journal 2024-12, p.1-1
Main Authors: Murovec, Jure, Prezelj, Jurij, Ciric, Dejan, Milivojcevic, Marko
Format: Article
Language:English
Subjects:
Acoustic signal analysis
Acoustics
Engines
Feature extraction
Harmonic analysis
Microphones
Power harmonic filters
Recording
Sensor phenomena and characterization
Sensors
Time domain
Time-domain analysis
Unsupervised learning
Vehicle classification
Zero crossing
Zero current switching
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Summary: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.
ISSN:1530-437X
DOI:10.1109/JSEN.2024.3516876