Recognition of sound vibration by DCNN based on φ-OTDR system

Cong Chen; Jiamin Li; Zujun Qin; Xianming Xiong; Wentao Zhang

doi:10.1117/12.2603867

24 November 2021 Recognition of sound vibration by DCNN based on φ-OTDR system

Cong Chen, Jiamin Li, Zujun Qin, Xianming Xiong, Wentao Zhang

Proceedings Volume 12066, AOPC 2021: Micro-optics and MOEMS; 120660K (2021) https://doi.org/10.1117/12.2603867
Event: Applied Optics and Photonics China 2021, 2021, Beijing, China

Abstract

Phase-sensitive optical time-domain reflectometer (φ-OTDR) is widely used for safety monitoring of large-scale civil objects, by which external vibrations along the sensing fiber can be detected. It has to be noticed that the category of vibration signals should be accurately distinguished for many real applications. At present, an extensively approach of signal recognition is deep convolutional neural network (DCNN). In the work, the one-dimensional DCNN (1D-DCNN) is applied to recognize different sound-induced vibrations based on their time-domain intensity signals detected by an amplitude-demodulated φ-OTDR system. It is turned out that the DCNN successfully shows the capability of recognizing walking, rock drill, explosion, hand hammer, car siren, and background noises with a high accuracy. Additionally, the 1D time-domain intensity vectors are rearranged into 2D matrices and the 2D-DCNN is accordingly employed to identify these vibration signals. The confusion matrices demonstrate that the 1D-DCNN has a higher average recognition accuracy to identify the concerned sounds with respect to the 2D-DCNN.

Citation Download Citation

Cong Chen, Jiamin Li, Zujun Qin, Xianming Xiong, and Wentao Zhang "Recognition of sound vibration by DCNN based on φ-OTDR system", Proc. SPIE 12066, AOPC 2021: Micro-optics and MOEMS, 120660K (24 November 2021); https://doi.org/10.1117/12.2603867

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