Copyright © 2008 The Institute of Electronics, Information and Communication Engineers
Special Section on Signal Processing for Audio and Visual Systems and Its Implementations -- Letter -- Engineering Acoustics |
Underwater Transient Signal Classification Using Binary Pattern Image of MFCC and Neural Network
1 The authors are with School of Electrical Engineering and Computer Science, Kyungpook National University, Sankyuk-dong, Buk-gu, Daegu, 702-701, Korea. E-mail: ksbae{at}mirbbs.knu.ac.kr, 2 The author is with Agency for Defense Development, P.O. Box 18, Chinhae, Kyungnam, 645-600, Korea.
This paper presents a new method for classification of underwater transient signals, which employs a binary image pattern of the mel-frequency cepstral coefficients as a feature vector and a feed-forward neural network as a classifier. The feature vector is obtained by taking DCT and 1-bit quantization for the square matrix of the mel-frequency cepstral coefficients that is derived from the frame based cepstral analysis. The classifier is a feed-forward neural network having one hidden layer and one output layer, and a back propagation algorithm is used to update the weighting vector of each layer. Experimental results with underwater transient signals demonstrate that the proposed method is very promising for classification of underwater transient signals.
Key Words: SONAR, underwater transient signal, MFCC, classification, neural network
Manuscript received July 6, 2007. Manuscript revised October 17, 2007.
Reference
[1] D. Carevic, "Adaptive window-length detection of underwater transients using wavelets," J. Acoust. Soc. Am., vol.117, no.5, pp.2904–2913, May 2005. [2] S. Tucker and G.J. Brown, "Classification of transient sonar sounds using perceptually motivated features," IEEE J. Ocean. Eng., vol.30, no.3, pp.588–600, July 2005. [3] S.K. Goumas, M.E. Zervakis, and G.S. Stavrakakis, "Classification of washing machines vibration signals using discrete wavelet analysis for feature extraction," IEEE Trans. Instrum. Meas., vol.51, no.3, pp.497–508, June 2002. [4] A. Sadjadi, S. Ghaloum, and R. Zoughi, "Terrain classification in SAR images using principal component analysis and neural networks," IEEE Trans. Geosci. Remote Sens., vol.31, no.2, pp.511–512, 1993. [5] J.R. Dellar, J.G. Proakis, and J.H.L. Hansen, Discrete-Time Processing of Speech Signals, pp.1380–1397, Macmillan Publishing Company. 1993. [6] K. Sri Rama Murty and B. Yegnanarayana, "Combining evidence from residual phase and MFCC features for speaker recognition," IEEE Signal Process. Lett., vol.13, no.1, pp.52–55, Jan. 2006.
![]()
CiteULike
Connotea
Del.icio.us What's this?
This Article ![]()
![]()
Abstract
![]()
Full Text (PDF)
![]()
Alert me when this article is cited
![]()
Alert me if a correction is posted
![]()
Services ![]()
![]()
Email this article to a friend
![]()
Similar articles in this journal
![]()
Alert me to new issues of the journal
![]()
Add to My Personal Archive
![]()
Download to citation manager
![]()
Request Permissions
![]()
Google Scholar ![]()
![]()
Articles by LIM, T.
![]()
Articles by LEE, H.
![]()
Search for Related Content
![]()
Social Bookmarking ![]()
![]()
What's this?