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Journal of Sensors
Volume 2016 (2016), Article ID 7864213, 11 pages
Research Article

Feature Extraction of Underwater Target Signal Using Mel Frequency Cepstrum Coefficients Based on Acoustic Vector Sensor

Lanyue Zhang,1,2 Di Wu,1,2 Xue Han,1,2 and Zhongrui Zhu1,2

1Science and Technology on Underwater Acoustic Laboratory, Harbin Engineering University, Harbin 150001, China
2College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China

Received 31 May 2016; Revised 8 September 2016; Accepted 11 October 2016

Academic Editor: Andreas Schütze

Copyright © 2016 Lanyue Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Feature extraction method using Mel frequency cepstrum coefficients (MFCC) based on acoustic vector sensor is researched in the paper. Signals of pressure are simulated as well as particle velocity of underwater target, and the features of underwater target using MFCC are extracted to verify the feasibility of the method. The experiment of feature extraction of two kinds of underwater targets is carried out, and these underwater targets are classified and recognized by Backpropagation (BP) neural network using fusion of multi-information. Results of the research show that MFCC, first-order differential MFCC, and second-order differential MFCC features could be used as effective features to recognize those underwater targets and the recognition rate, which using the particle velocity signal is higher than that using the pressure signal, could be improved by using fusion features.