结合基于规则的波器和机器学习的混合方法,从脉冲事件记录器检测海豚和船舶的声音
Mayu I Ogawa1,2, Satoko S Kimura3,4, Nozomu Ishiai3
1Underwater Biological Sound Analysis Group, Smart Sensing Technology Development Center, Research Institute for Marine Technology and Engineering, Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokosuka, Kanagawa, Japan. mayu.ogawa.88@gmail.com.
Scientific reports
|August 25, 2025
概括
一种新的混合方法使用声学数据有效地检测无海豚点击列车和船只噪音. 这种方法大大减少了假阳性,有助于海洋生态系统的监测.
科学领域:
- 海洋生物学
- 生物声学
- 信号处理
背景情况:
- 对于评估海洋生态系统的健康状况和检测海洋生物而言,被动声学监测至关重要.
- 传统的算法难以处理来自声学记录器的复杂脉冲事件数据,
研究的目的:
- 开发一种混合分析框架,以有效地检测狭无海豚 (Neophocaena asiaeorientalis) 的点击列车和船只噪音.
- 改进脉冲事件记录器,特别是A-tag的数据分析.
主要方法:
- 一个混合框架结合了基于规则的过器和随机森林模型.
- 基于规则的过器预处理原始数据以减少噪音.
- 使用随机森林模型对检测到的事件进行分类.
主要成果:
- 基于规则的过器实现了高初始检测精度 (点击列车近100%,船舶噪声94%) 但具有高的错误阳性率 (45%和81%).
- 随机森林模型显著提高了对点击列车的分类准确度,达到97%和船舶噪声的99%.
- 混合方法极大地降低了错误的阳性率,为2.8%的点击列车和0.1%的船只噪声.
结论:
- 结合基于规则的过器和随机森林模型提供了处理脉冲事件记录器数据的强大而高效的方法.
- 这种方法提高了检测准确度,减少了手工工作量,促进了对人为噪声影响的快速评估.
- 该方法支持在具有挑战性的海洋环境中对小型鱼种群进行长期生态监测.
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