Convolutional neural network for detecting odontocete echolocation clicks.

Journal: The Journal of the Acoustical Society of America
PMID:

Abstract

In this work, a convolutional neural network based method is proposed to automatically detect odontocetes echolocation clicks by analyzing acoustic data recordings from a passive acoustic monitoring system. The neural network was trained to distinguish between click and non-click clips and was subsequently converted to a full-convolutional network. The performance of the proposed network was evaluated using synthetic data and real audio recordings. The experimental results indicate that the proposed method works stably with echolocation clicks of different species.

Authors

  • Wenyu Luo
    Key Laboratory of Underwater Acoustic Communication and Marine Information Technology of the Ministry of Education, College of Ocean and Earth Science, Xiamen University, Xiamen, Chinaluowenyu@stu.xmu.edu.cn, wyyang@xmu.edu.cn, yuzhang@xmu.edu.cn.
  • Wuyi Yang
    Key Laboratory of Underwater Acoustic Communication and Marine Information Technology of the Ministry of Education, College of Ocean and Earth Science, Xiamen University, Xiamen, Chinaluowenyu@stu.xmu.edu.cn, wyyang@xmu.edu.cn, yuzhang@xmu.edu.cn.
  • Yu Zhang
    College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China.