A study on DEMONgram frequency line extraction method using deep learning

Wonsik Shin; Hyuckjong Kwon; Hoseok Sul; Won Shin; Hyunsuk Ko; Taek-Lyul Song; Da-Sol Kim; Kang-Hoon Choi; Jee Woong Choi

doi:10.7776/ASK.2024.43.1.078

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2024 Vol.43, Issue 1 Preview Page Next Page

Research Article

A study on DEMONgram frequency line extraction method using deep learning 딥러닝을 이용한 DEMON 그램 주파수선 추출 기법 연구

31 January 2024. pp. 78-88

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Abstract

Ship-radiated noise received by passive sonar that can measure underwater noise can be identified and classified ship using Detection of Envelope Modulation on Noise (DEMON) analysis. However, in a low Signal-to-Noise Ratio (SNR) environment, it is difficult to analyze and identify the target frequency line containing ship information in the DEMONgram. In this paper, we conducted a study to extract target frequency lines using semantic segmentation among deep learning techniques for more accurate target identification in a low SNR environment. The semantic segmentation models U-Net, UNet++, and DeepLabv3+ were trained and evaluated using simulated DEMONgram data generated by changing SNR and fundamental frequency, and the DEMONgram prediction performance of DeepShip, a dataset of ship-radiated noise recordings on the strait of Georgia in Canada, was compared using the trained models. As a result of evaluating the trained model with the simulated DEMONgram, it was confirmed that U-Net had the highest performance and that it was possible to extract the target frequency line of the DEMONgram made by DeepShip to some extent.

Keywords

Deep learning

Semantic segmentation

Detection of Envelope Modulation on Noise (DEMON)

DEMONgram

Frequency line extraction

수중 소음 측정이 가능한 수동 소나에 수신된 선박 방사소음은 Detection of Envelope Modulation on Noise(DEMON) 분석으로 얻은 선박 정보를 사용하여 선박 식별과 분류가 가능하다. 하지만 낮은 신호대잡음비(Signal-to-Noise Ratio, SNR) 환경에서는 DEMON 그램 내 선박 정보가 담겨있는 표적 주파수선을 분석 및 파악하는데 어려움이 발생한다. 본 논문에서는 낮은 SNR 환경에서 보다 정확한 표적 식별을 위해 딥러닝 기법 중 의미론적 분할을 사용하여 표적 주파수선들을 추출하는 연구를 수행하였다. SNR과 기본 주파수를 변경시키며 생성한 모의 DEMON 그램 데이터를 사용하여 의미론적 분할 모델인 U-Net, UNet++, DeepLabv3+를 학습 후 평가하였고, 학습된 모델들을 이용하여 캐나다 조지아 해협에서 측정한 선박 방사소음 데이터셋인 DeepShip으로 제작한 DEMON 그램 예측 성능을 비교하였다. 모의 DEMON 그램으로 학습된 모델을 평가한 결과 U-Net이 성능이 가장 높았으며, DeepShip으로 만든 DEMON 그램의 표적 주파수선을 어느 정도 추출할 수 있는 것을 확인하였다.

키워드

딥러닝

의미론적 분할

Detection of Envelope Modulation on Noise (DEMON)

DEMON 그램

주파수선 추출

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Information

Publisher :The Acoustical Society of Korea
Publisher(Ko) :한국음향학회
Journal Title :The Journal of the Acoustical Society of Korea
Journal Title(Ko) :한국음향학회지
Volume : 43
No :1
Pages :78-88
Received Date : 2023-09-04
Revised Date : 2023-11-23
Accepted Date : 2023-11-24
DOI :https://doi.org/10.7776/ASK.2024.43.1.078

The Journal of the Acoustical Society of KoreaISSN:1225-4428(Print) 2287-3775(Online)한국음향학회

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