Key Laboratory of Underwater Acoustic Communication and Marine Information Technology of the Ministry of Education, College of Ocean and Earth Science, Xiamen University, Xiamen,
J Acoust Soc Am. 2019 Jan;145(1):EL7. doi: 10.1121/1.5085647.
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.
在这项工作中,提出了一种基于卷积神经网络的方法,通过分析被动声学监测系统的声学数据记录来自动检测齿鲸的回声定位点击。该神经网络经过训练,能够区分点击和非点击片段,随后被转换为全卷积网络。使用合成数据和真实音频记录评估了所提出的网络的性能。实验结果表明,该方法可以稳定地处理不同物种的回声定位点击。