Geophysics and Geophysical Exploration (지구물리와물리탐사)

Korean Society of Earth and Exploration Geophysicists (한국지구물리·물리탐사학회)
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Development of Dolphin Click Signal Classification Algorithm Based on Recurrent Neural Network for Marine Environment Monitoring

해양환경 모니터링을 위한 순환 신경망 기반의 돌고래 클릭 신호 분류 알고리즘 개발

  • Seoje Jeong (Department of Convergence Study on the Ocean Science and Technology, Ocean Science and Technology (OST) School, Korea Maritime & Ocean University) ;
  • Wookeen Chung (Department of Energy and Resources Engineering, Korea Maritime & Ocean University) ;
  • Sungryul Shin (Department of Energy and Resources Engineering, Korea Maritime & Ocean University) ;
  • Donghyeon Kim (Underwater Vehicle Research Center, Korea Maritime & Ocean University) ;
  • Jeasoo Kim (Department of Ocean Engineering, Korea Maritime & Ocean University) ;
  • Gihoon Byun (Department of Convergence Study on the Ocean Science and Technology, Ocean Science and Technology (OST) School, Korea Maritime & Ocean University) ;
  • Dawoon Lee (Department of Energy and Resources Engineering, Korea Maritime & Ocean University)
  • 정서제 (한국해양대학교 해양과학기술융합학과) ;
  • 정우근 (한국해양대학교 에너지자원공학과) ;
  • 신성렬 (한국해양대학교 에너지자원공학과) ;
  • 김동현 (한국해양대학교 수중운동체특화연구센터) ;
  • 김재수 (한국해양대학교 해양공학과) ;
  • 변기훈 (한국해양대학교 해양과학기술융합학과) ;
  • 이다운 (한국해양대학교 에너지자원공학과)
  • Received : 2023.07.03
  • Accepted : 2023.08.10
  • Published : 2023.08.31
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Abstract

In this study, a recurrent neural network (RNN) was employed as a methodological approach to classify dolphin click signals derived from ocean monitoring data. To improve the accuracy of click signal classification, the single time series data were transformed into fractional domains using fractional Fourier transform to expand its features. Transformed data were used as input for three RNN models: long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (BiLSTM), which were compared to determine the optimal network for the classification of signals. Because the fractional Fourier transform displayed different characteristics depending on the chosen angle parameter, the optimal angle range for each RNN was first determined. To evaluate network performance, metrics such as accuracy, precision, recall, and F1-score were employed. Numerical experiments demonstrated that all three networks performed well, however, the BiLSTM network outperformed LSTM and GRU in terms of learning results. Furthermore, the BiLSTM network provided lower misclassification than the other networks and was deemed the most practically appliable to field data.

본 연구에서는 해양 모니터링 중에 기록된 돌고래 클릭 신호를 분류하기 위해 순환 신경망(RNN)을 적용하는 방법을 검토했다. 클릭 신호 분류의 정확도를 높이기 위해 단일 시계열 자료를 분수 푸리에 변환을 사용하여 분수 영역으로 변환하여 특징을 확장했으며, 분류를 위한 최적의 네트워크를 결정하기 위해 세 가지 순환 신경망 모델(LSTM, GRU, BiLSTM)을 비교 분석하였다. 순환 신경망 모델의 입력 자료로써 이용된 분수 영역 자료의 경우, 분수 푸리에 변환 시 회전 각도에 따라 다른 특성을 가지므로, 각 네트워크 모델에 따라 우수한 성능을 가지는 회전 각도 범위를 분석했다. 이때 네트워크 성능 분석을 위해 정확도, 정밀도, 재현율, F1-점수와 같은 성능 평가 지표를 도입했다. 수치실험 결과, 세 가지 네트워크 모두 높은 성능을 보였으며, BiLSTM 네트워크가 LSTM, GRU에 비해 뛰어난 학습 결과를 제공했다. 마지막으로, 현장 자료 적용 가능성 측면에서 BiLSTM 네트워크가 다른 네트워크에 비해 낮은 오탐지 결과를 제공하였다.

Keywords

Acknowledgement

이 논문은 2023년도 해양수산부 재원으로 해양수산과학기술진흥원의 지원을 받아 수행된 연구임(RS-2023-00259633).

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