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

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概要

• 論文の詳細を見る

• 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.

• （社）電子情報通信学会の論文
• 2008-03-01

著者

• BAE Keunsung

  School of Electrical Engineering and Computer Science, Kyungpook National University

• Bae Keunsung

  School Of Electrical Engineering And Computer Science Kyungpook National University

• LIM Taegyun

  School of Electrical Engineering and Computer Science, Kyungpook National University

• HWANG Chansik

  School of Electrical Engineering and Computer Science, Kyungpook National University

• LEE Hyeonguk

  Agency for Defense Development

• Hwang Chansik

  Kyungpook National Univ. Daegu Kor

• Hwang Chansik

  School Of Electrical Engineering And Computer Science Kyungpook National University

• Lim Taegyun

  School Of Electrical Engineering And Computer Science Kyungpook National University

• Bae Keunsung

  Kyungpook National Univ. Daegu Kor

• Bae Keunsung

  School Of Electrical Engineering And Computer Sci. Kyungpook National Univ.

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