The Journal of the Acoustical Society of Korea (한국음향학회지)

The Acoustical Society of Korea (한국음향학회)
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Preprocessing performance of convolutional neural networks according to characteristic of underwater targets

수중 표적 분류를 위한 합성곱 신경망의 전처리 성능 비교

  • 박경민 (한국해양대학교 해양공학과) ;
  • 김두영 (대한민국 해군사관학교 사이버과학과)
  • Received : 2022.07.21
  • Accepted : 2022.10.05
  • Published : 2022.11.30
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Abstract

We present a preprocessing method for an underwater target detection model based on a convolutional neural network. The acoustic characteristics of the ship show ambiguous expression due to the strong signal power of the low frequency. To solve this problem, we combine feature preprocessing methods with various feature scaling methods and spectrogram methods. Define a simple convolutional neural network model and train it to measure preprocessing performance. Through experiment, we found that the combination of log Mel-spectrogram and standardization and robust scaling methods gave the best classification performance.

본 논문은 합성곱 신경망 기반 수중 표적 분류기의 성능 향상을 위한 최적의 전처리 기법을 제시한다. 실제 선박 수중신호를 수집한 데이터 세트의 주파수 분석을 통해 강한 저주파 신호로 인한 특성 표현의 문제점을 확인하였다. 이를 해결하기 위해 다양한 스펙트로그램 기법과 특성 스케일링 기법을 조합한 전처리 기법들을 구현하였다. 최적의 전처리 기법을 확인하기 위해 실제 데이터를 기반으로 합성곱 신경망을 훈련하는 실험을 수행하였다. 실험 결과, 로그 멜 스펙트로그램과 표준화 및 로버스트정규화 스케일링 기법의 조합이 높은 인식 성능과 빠른 학습 속도를 보임을 확인하였다.

Keywords

Acknowledgement

이 논문은 2022년 해군사관학교 해양연구소 학술연구과제 연구비 지원으로 수행된 연구임.

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