Feature Extraction of Underwater Target Signal Using Mel Frequency Cepstrum Coefficients Based on Acoustic Vector Sensor
Joint Authors
Zhang, Lanyue
Wu, Di
Han, Xue
Zhu, Zhongrui
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-11-17
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Zhang, Lanyue& Wu, Di& Han, Xue& Zhu, Zhongrui. 2016. Feature Extraction of Underwater Target Signal Using Mel Frequency Cepstrum Coefficients Based on Acoustic Vector Sensor. Journal of Sensors،Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1110613
Modern Language Association (MLA)
Zhang, Lanyue…[et al.]. Feature Extraction of Underwater Target Signal Using Mel Frequency Cepstrum Coefficients Based on Acoustic Vector Sensor. Journal of Sensors No. 2016 (2016), pp.1-11.
https://search.emarefa.net/detail/BIM-1110613
American Medical Association (AMA)
Zhang, Lanyue& Wu, Di& Han, Xue& Zhu, Zhongrui. Feature Extraction of Underwater Target Signal Using Mel Frequency Cepstrum Coefficients Based on Acoustic Vector Sensor. Journal of Sensors. 2016. Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1110613
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1110613