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Vol.12, No.3, August 2023. ISSN: 2217-8309 eISSN: 2217-8333
TEM Journal
TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS Association for Information Communication Technology Education and Science |
Acoustic Vehicle Classification Using Mel-Frequency Features with Long Short-Term Memory Neural Networks
Ahmad Ihsan Yassin, Khairul Khaizi Mohd Shariff, Mustapha Awang Kechik, Adli Md Ali, Megat Syahirul Megat Amin
© 2023 Khairul Khaizi Mohd Shariff, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)
Citation Information: TEM Journal. Volume 12, Issue 3, Pages 1490-1496, ISSN 2217-8309, DOI: 10.18421/TEM123-29, August 2023.
Received: 09 April 2023. Revised: 14 May 2023.
Abstract:
Monitoring vehicle traffic at a large scale is a challenging task for authorities, particularly considering the high cost of traffic sensors such as vision cameras. To meet the growing demand for more accurate traffic monitoring, the use of traffic sounds has become a popular approach, as it provides insight into the types of traffic present. This paper reports on an approach to vehicle classification based on acoustic signals, using the Mel-Frequency Cepstral Coefficients (MFCC) and the Long Short-Term Memory (LSTM) networks. This study exhibited classification accuracy scores of 82-86.2% across four vehicle categories: motorcycle, car, truck, and no traffic. The results demonstrated that large-scale, low-cost acoustic processing can be effectively used for vehicle monitoring.
Keywords –Acoustic vehicle classification, long short-term memory (LSTM), acoustic traffic noise, mel-cepstral frequency features (MFCC), Machine learning. |
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