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

Kohei Niwayama, Kenji Muto, Yosuke Kobayashi

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルGCCE 2022 - 2022 IEEE 11th Global Conference on Consumer Electronics
出版社Institute of Electrical and Electronics Engineers Inc.
ページ3-4
ページ数2
ISBN(電子版)9781665492324
DOI
出版ステータスPublished - 2022
イベント11th IEEE Global Conference on Consumer Electronics, GCCE 2022 - Osaka, Japan
継続期間: 2022 10月 182022 10月 21

出版物シリーズ

名前GCCE 2022 - 2022 IEEE 11th Global Conference on Consumer Electronics

Conference

Conference11th IEEE Global Conference on Consumer Electronics, GCCE 2022
国/地域Japan
CityOsaka
Period22/10/1822/10/21

ASJC Scopus subject areas

  • 信号処理
  • 情報システムおよび情報管理
  • 電子工学および電気工学
  • メディア記述
  • 器械工学
  • 社会心理学

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