Proceedings of the IEEK Conference (대한전자공학회:학술대회논문집)

The Institute of Electronics and Information Engineers (대한전자공학회)
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Frame Based Classification of Underwater Transient Signal Using MFCC Feature Vector and Neural Network

MFCC 특징벡터와 신경회로망을 이용한 프레임 기반의 수중 천이신호 식별

  • Lim, Tae-Gyun (Samsung Electronics Co. Ltd.) ;
  • Kim, Il-Hwan (School of Electrical Engineering and Computer Science Kyungpook National University) ;
  • Kim, Tae-Hwan (School of Electrical Engineering and Computer Science Kyungpook National University) ;
  • Bae, Keun-Sung (School of Electrical Engineering and Computer Science Kyungpook National University)
  • 임태균 (삼성전자) ;
  • 김일환 (경북대학교 전자전기컴퓨터학부) ;
  • 김태환 (경북대학교 전자전기컴퓨터학부) ;
  • 배건성 (경북대학교 전자전기컴퓨터학부)
  • Published : 2008.06.18
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Abstract

This paper presents a method for classification of underwater transient signals using, which employs a binary image pattern of the mel-frequency cepstral coefficients(MFCC) as a feature vector and a neural network as a classifier. A feature vector is obtained by taking DCT and 1-bit quantization for the square matrix of the MFCC sequences. 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 some underwater transient signals demonstrate that the proposed method is very promising for classification of underwater transient signals.

Keywords