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IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences 2008 E91-A(3):772-774; doi:10.1093/ietfec/e91-a.3.772
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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

Taegyun LIM1, Keunsung BAE1, Chansik HWANG1 and Hyeonguk LEE2

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.


  Abstract

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.


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