| Title |
CNN-based Shipping Noise Detection using Short-time Underwater Acoustics Signal |
| Authors |
김범규(Bum-Kyu Kim) ; 장원두(Won-Du Chang) ; 김한수(Hansoo Kim) ; 김선효(Sunhyo Kim) ; 강돈혁(Donhyug Kang) ; 김미라(Mira Kim) ; 강동현(Dong-Hyun Kang) ; 조성호(Sungho Cho) |
| DOI |
https://doi.org/10.5573/ieie.2023.60.3.61 |
| Keywords |
Underwater acoustics; Shipping noise; Deep learning; Convolutional neural network (CNN) |
| Abstract |
Most of the noise generated by humans in the ocean is ship radiated noise caused to fishing and commercial shipping. Recently, deep learning technology has been used to detect shipping noise. In this study, the convolutional neural network is trained by a shipping noise spectrogram divided into 1-minute units to detect a near distance ship. Inception-V3, ResNet-50, VGG-16 and the proposed model were used to learn and evaluate 1-minute shipping noise. As a result, the F1 scores were 97.42%, 98.42%, 98.16% and 97.88% for Inception-V3, ResNet-50, VGG-16 and the proposed model, respectively. These models showed satisfactory performance in detecting shipping noise. It was confirmed that the proposed model showed equivalent detection performance with about 1/8 parameters compared to ResNet-50. For future works, it is expected that it will be possible to detect long-distance shipping noise by using additional noise data and AIS(Automatic Identification System). |