Convolutional Recurrent Neural Network-Based Boat Detection Method for Wind Noise Condition


연구 분야: Artificial Intelligence



학회: 2022 IEEE 11th Global Conference on Consumer Electronics (GCCE)


초록

We have been working on boat detection from environmental sound using a convolutional neural network (CNN). However, it had a problem with accuracy degrading when strong winds blew. In this study, we propose a method for boat detection using deep learning from environmental sound in strong wind conditions. Our proposal method was boat detection via convolutional recurrent neural network using the difference in duration between the boat and wind noise as a cue. The improvement of the proposed method was 0.03 points higher on the average of F-measure than the CNN.


Author Profile
Kohei Niwayama

Graduate School of Science and Engineering Shibaura Institute of Technology Tokyo Japan

Andorra
Author Profile
Kenji Muto

Department of Information and Communications Engineering Shibaura Institute of Technology Tokyo Japan

Andorra
Author Profile
Yosuke Kobayashi

Graduate School of Engineering Muroran Institute of Technology Hokkaido Japan

Japan

📄 논문 정보

발행 연도 2022년
인용수 140
출판 국가 Andorra, Japan
사이트 IEEE
좋아요 수 0

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