Vehicle detection and classification by voiceprint recognition based on single acoustic sensor under bridge expansion joint
Vehicle detection and classification (VDC) is a crucial aspect of bridge engineering, as vehicles exert significant dynamic loads on bridges. Various contact and noncontact methods have been proposed for VDC, but finding a durable and cost-effective sensing approach remains challenging for practical...
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sg-ntu-dr.10356-1807692024-10-23T05:21:48Z Vehicle detection and classification by voiceprint recognition based on single acoustic sensor under bridge expansion joint Pan, Yue Dong, Yiqing Wang, Dalei Di, Jin Chen, Airong School of Civil and Environmental Engineering Engineering Acoustic sensing Deep learning Vehicle detection and classification (VDC) is a crucial aspect of bridge engineering, as vehicles exert significant dynamic loads on bridges. Various contact and noncontact methods have been proposed for VDC, but finding a durable and cost-effective sensing approach remains challenging for practical bridge applications. In this study, we propose a voiceprint recognition (VPR) method for VDC using a single microphone, offering flexibility and affordability. The distinctive acoustic signatures generated by vehicles impacting bridge expansion joints (BEJs) are captured and utilized for VDC. We optimize a threshold-based algorithm for vehicle detection and a deep learning-based VPR model for vehicle classification. In addition, cascading VPR models enable fine-grained vehicle classification, including lanes and axle types. We validate the proposed method on an actual bridge. The short-term energy (STE) thresholding algorithm achieves a detection accuracy and recall of 90.0% and 91.6%, respectively. The ConFormer model achieves an area under the curve (AuC) of 0.925 for vehicle classification. These results highlight the method as an easy-to-implement and cost-effective solution for efficient VDC surveys. Future work can focus on expanding datasets and incorporating multiple microphones to further enhance the system's capabilities. This work was supported in part by the National Natural Science Foundation of China under Grant 52208198, Grant 52238005, and Grant 52192663; in part by the National Key Research and Development Program of China under Grant 2021YFF0501004; and in part by the Interdisciplinary Project in Ocean Research of Tongji University under Grant 2023-1-YB-03. 2024-10-23T05:17:02Z 2024-10-23T05:17:02Z 2024 Journal Article Pan, Y., Dong, Y., Wang, D., Di, J. & Chen, A. (2024). Vehicle detection and classification by voiceprint recognition based on single acoustic sensor under bridge expansion joint. IEEE Transactions On Instrumentation and Measurement, 73, 1-15. https://dx.doi.org/10.1109/TIM.2024.3428610 0018-9456 https://hdl.handle.net/10356/180769 10.1109/TIM.2024.3428610 2-s2.0-85198713333 73 1 15 en IEEE Transactions on Instrumentation and Measurement © 2024 IEEE. All rights reserved. |
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Engineering Acoustic sensing Deep learning Pan, Yue Dong, Yiqing Wang, Dalei Di, Jin Chen, Airong Vehicle detection and classification by voiceprint recognition based on single acoustic sensor under bridge expansion joint |
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Vehicle detection and classification (VDC) is a crucial aspect of bridge engineering, as vehicles exert significant dynamic loads on bridges. Various contact and noncontact methods have been proposed for VDC, but finding a durable and cost-effective sensing approach remains challenging for practical bridge applications. In this study, we propose a voiceprint recognition (VPR) method for VDC using a single microphone, offering flexibility and affordability. The distinctive acoustic signatures generated by vehicles impacting bridge expansion joints (BEJs) are captured and utilized for VDC. We optimize a threshold-based algorithm for vehicle detection and a deep learning-based VPR model for vehicle classification. In addition, cascading VPR models enable fine-grained vehicle classification, including lanes and axle types. We validate the proposed method on an actual bridge. The short-term energy (STE) thresholding algorithm achieves a detection accuracy and recall of 90.0% and 91.6%, respectively. The ConFormer model achieves an area under the curve (AuC) of 0.925 for vehicle classification. These results highlight the method as an easy-to-implement and cost-effective solution for efficient VDC surveys. Future work can focus on expanding datasets and incorporating multiple microphones to further enhance the system's capabilities. |
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School of Civil and Environmental Engineering |
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School of Civil and Environmental Engineering Pan, Yue Dong, Yiqing Wang, Dalei Di, Jin Chen, Airong |
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Article |
author |
Pan, Yue Dong, Yiqing Wang, Dalei Di, Jin Chen, Airong |
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Pan, Yue |
title |
Vehicle detection and classification by voiceprint recognition based on single acoustic sensor under bridge expansion joint |
title_short |
Vehicle detection and classification by voiceprint recognition based on single acoustic sensor under bridge expansion joint |
title_full |
Vehicle detection and classification by voiceprint recognition based on single acoustic sensor under bridge expansion joint |
title_fullStr |
Vehicle detection and classification by voiceprint recognition based on single acoustic sensor under bridge expansion joint |
title_full_unstemmed |
Vehicle detection and classification by voiceprint recognition based on single acoustic sensor under bridge expansion joint |
title_sort |
vehicle detection and classification by voiceprint recognition based on single acoustic sensor under bridge expansion joint |
publishDate |
2024 |
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https://hdl.handle.net/10356/180769 |
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1814777761050394624 |