Underwater Transient Signal Classification Using Binary Pattern Image of MFCC and Neural Network

Taegyun LIM  Keunsung BAE  Chansik HWANG  Hyeonguk LEE  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E91-A   No.3   pp.772-774
Publication Date: 2008/03/01
Online ISSN: 1745-1337
DOI: 10.1093/ietfec/e91-a.3.772
Print ISSN: 0916-8508
Type of Manuscript: Special Section LETTER (Special Section on Signal Processing for Audio and Visual Systems and Its Implementations)
Category: Engineering Acoustics
SONAR,  underwater transient signal,  MFCC,  classification,  neural network,  

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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.