Journal of the Institute of Convergence Signal Processing (융합신호처리학회논문지)

The Korea Institute of Convergence Signal Processing (한국융합신호처리학회)
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Classification of Whale Sounds using LPC and Neural Networks

신경망과 LPC 계수를 이용한 고래 소리의 분류

  • An, Woo-Jin (School of Information Technology Convergence, University of Ulsan) ;
  • Lee, Eung-Jae (School of Information Technology Convergence, University of Ulsan) ;
  • Kim, Nam-Gyu (School of Information Technology Convergence, University of Ulsan) ;
  • Chong, Ui-Pil (School of Information Technology Convergence, University of Ulsan)
  • Received : 2017.11.07
  • Accepted : 2017.12.20
  • Published : 2017.12.31
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Abstract

The underwater transients signals contain the characteristics of complexity, time varying, nonlinear, and short duration. So it is very hard to model for these signals with reference patterns. In this paper we separate the whole length of signals into some short duration of constant length with overlapping frame by frame. The 20th LPC(Linear Predictive Coding) coefficients are extracted from the original signals using Durbin algorithm and applied to neural network. The 65% of whole signals were learned and 35% of the signals were tested in the neural network with two hidden layers. The types of the whales for sound classification are Blue whale, Dulsae whale, Gray whale, Humpback whale, Minke whale, and Northern Right whale. Finally, we could obtain more than 83% of classification rate from the test signals.

수중천이신호는 복잡하고 시변, 비선형 및 짧은 지속성의 특성을 지니고 있어서 기준패턴으로 모델링하기가 어렵다. 본 논문에서는 이러한 신호들을 프레임간의 중첩을 허용하는 일정한 짧은 신호로 잘라서 분석한다. 더빈 알고리듬을 이용하여 20차의 선형예측계수(LPC)를 프레임마다 추출하여 2층 은닉신경망회로의 입력신호로 사용한다. 추출된 선형예측계수들의 65%는 신경망구조의 학습에 이용되고 35%는 시험용 입력신호로 사용된다. 고래소리 분류에 사용된 고래 종류는 대왕고래, 들쇠고래, 귀신고래, 혹등고래, 밍크고래, 북방긴수염고래 등이다. 결과적으로 이러한 시험용의 신호들로부터 83%이상의 고래 소리 평균 분류율을 얻을 수 있었다.

Keywords

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