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An event recognition method for fiber distributed acoustic sensing systems based on the combination of MFCC and CNN
Author(s): Fei Jiang; Honglang Li; Zhenhai Zhang; Xuping Zhang
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Paper Abstract

Fiber distributed acoustic sensing (FDAS) systems have been widely used in many fields such as oil and gas pipeline monitoring, urban safety monitoring, and perimeter security. An event recognition method for fiber distributed acoustic sensing (FDAS) systems is proposed in this paper. The Mel-frequency cepstrum coefficients (MFCC) of the acoustic signals collected by the FDAS system are computed as the features of the events, which are inputted into a convolutional neural network (CNN) to determine the type of the events. Experimental results based on 2300 training samples and 946 test samples show that the precision, recall, and f1-score of the classification model reach as high as 98.02%, 97.99%, and 97.98% respectively, which means that the combination of MFCC and CNN may be a promising event recognition method for FDAS systems.

Paper Details

Date Published: 10 January 2018
PDF: 7 pages
Proc. SPIE 10618, 2017 International Conference on Optical Instruments and Technology: Advanced Optical Sensors and Applications, 1061804 (10 January 2018); doi: 10.1117/12.2286220
Show Author Affiliations
Fei Jiang, Beijing Institute of Technology (China)
Institute of Acoustics (China)
Honglang Li, Institute of Acoustics (China)
Zhenhai Zhang, Beijing Institute of Technology (China)
Xuping Zhang, Nanjing Univ. (China)


Published in SPIE Proceedings Vol. 10618:
2017 International Conference on Optical Instruments and Technology: Advanced Optical Sensors and Applications
Xuping Zhang; Hai Xiao; Francisco Javier Arregui; Liquan Dong, Editor(s)

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