Classification of bearded seals signal based on convolutional neural network

Ji Seop Kim; Young Geul Yoon; Dong-Gyun Han; Hyoung Sul La; Jee Woong Choi

doi:10.7776/ASK.2022.41.2.235

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2022 Vol.41, Issue 2 Preview Page Next Page

Research Article

Classification of bearded seals signal based on convolutional neural network Convolutional neural network 기법을 이용한 턱수염물범 신호 판별

31 March 2022. pp. 235-241

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Abstract

Several studies using Convolutional Neural Network (CNN) have been conducted to detect and classify the sounds of marine mammals in underwater acoustic data collected through passive acoustic monitoring. In this study, the possibility of automatic classification of bearded seal sounds was confirmed using a CNN model based on the underwater acoustic spectrogram images collected from August 2017 to August 2018 in East Siberian Sea. When only the clear seal sound was used as training dataset, overfitting due to memorization was occurred. By evaluating the entire training data by replacing some training data with data containing noise, it was confirmed that overfitting was prevented as the model was generalized more than before with accuracy (0.9743), precision (0.9783), recall (0.9520). As a result, the performance of the classification model for bearded seals signal has improved when the noise was included in the training data.

Keywords

Passive acoustic monitoring

Bearded seal

Deep learning

Convolution neural network

Classification

수동 음향 관측을 통해 수집된 방대한 양의 데이터에서 해양포유류의 소리를 탐지하고 식별하기 위해 합성곱 신경망(Convolutional Neural Network, CNN)을 활용한 연구가 많이 수행되고 있다. 본 연구는 2017년 8월부터 2018년 8월까지 동시베리아 해에서 수집된 수중음향 스펙트럼 이미지를 기반으로 CNN을 활용하여 턱수염물범 소리의 분류 자동화 가능성을 확인해 보았다. 학습 데이터로서 다른 소음이 거의 포함되지 않은 뚜렷한 턱수염물범 소리를 사용하였을 때, 암기로 인한 과적합이 발생하였다. 일부 데이터를 소음이 포함된 데이터로 교체하여 학습시켜 수집된 전체 데이터로 평가한 결과 정확도(0.9743), 정밀도(0.9783), 재현율(0.9520)으로 모델이 이전보다 일반화되어 과적합이 방지되는 것을 확인하였다. 본 연구를 통해 물범신호 분류는 학습 데이터에 소음이 포함되었을 때 성능이 증가하는 것으로 나타났다.

키워드

수동 음향 관측

턱수염물범

딥러닝

합성곱 신경망

식별

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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 : 41
No :2
Pages :235-241
Received Date : 2022-01-18
Revised Date : 2022-02-25
Accepted Date : 2022-03-04
DOI :https://doi.org/10.7776/ASK.2022.41.2.235

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

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