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Proceedings Paper

Research on the feature extraction and pattern recognition of the distributed optical fiber sensing signal
Author(s): Bingjie Wang; Qi Sun; Shaohua Pi; Hongyan Wu
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Paper Abstract

In this paper, feature extraction and pattern recognition of the distributed optical fiber sensing signal have been studied. We adopt Mel-Frequency Cepstral Coefficient (MFCC) feature extraction, wavelet packet energy feature extraction and wavelet packet Shannon entropy feature extraction methods to obtain sensing signals (such as speak, wind, thunder and rain signals, etc.) characteristic vectors respectively, and then perform pattern recognition via RBF neural network. Performances of these three feature extraction methods are compared according to the results. We choose MFCC characteristic vector to be 12-dimensional. For wavelet packet feature extraction, signals are decomposed into six layers by Daubechies wavelet packet transform, in which 64 frequency constituents as characteristic vector are respectively extracted. In the process of pattern recognition, the value of diffusion coefficient is introduced to increase the recognition accuracy, while keeping the samples for testing algorithm the same. Recognition results show that wavelet packet Shannon entropy feature extraction method yields the best recognition accuracy which is up to 97%; the performance of 12-dimensional MFCC feature extraction method is less satisfactory; the performance of wavelet packet energy feature extraction method is the worst.

Paper Details

Date Published: 12 September 2014
PDF: 12 pages
Proc. SPIE 9193, Novel Optical Systems Design and Optimization XVII, 91930C (12 September 2014); doi: 10.1117/12.2060517
Show Author Affiliations
Bingjie Wang, Fudan Univ. (China)
Qi Sun, Fudan Univ. (China)
Shaohua Pi, Fudan Univ. (China)
Hongyan Wu, Fudan Univ. (China)


Published in SPIE Proceedings Vol. 9193:
Novel Optical Systems Design and Optimization XVII
G. Groot Gregory; Arthur J. Davis, Editor(s)

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