使用机器学习和概率反向投影检测和定位布莱德鱼的呼叫
Jean Baptiste Tary1, Sergio F Poveda2, Ka Lok Li1
1Geophysics Section, School of Cosmic Physics, Dublin Institute for Advanced Studies, Dublin, Ireland.
The Journal of the Acoustical Society of America
|August 22, 2025
概括
研究人员开发了一种机器学习方法, 这种方法可以在杂的海洋环境中准确地发现鱼的叫声.
科学领域:
- 海洋生物学
- 生物声学
- 机器学习
背景情况:
- 通过发声分析了解鱼的行为,
- 准确检测和定位鱼呼叫是PAM数据解释的关键挑战.
研究的目的:
- 开发和验证机器学习 (ML) 方法来检测和定位布莱德鱼的叫声,使用海洋底部的水声.
- 评估基于机器学习的本地化技术在现实声学条件下的稳定性和效率.
主要方法:
- 在增强的Bryde鱼呼叫数据 (890,214个例子) 上训练了一种新的ML模型.
- 检测值被优化,以平衡错误的阳性和阴性.
- 通过将交叉相关数据反向投射到3D网格来定位声学事件.
主要成果:
- 该方法成功检测到4514个潜在的鱼呼叫事件,其中899个使用至少3个水声器的数据进行本地化.
- 本地化程序证明了对高噪声,时间错误和速度模型偏差的稳定性.
- 这一系统在计算上被证明是高效的,
结论:
- 开发的基于ML的被动声学监测系统提供了一种有效和强大的方法来检测和定位布莱德的声音.
- 这种方法为海洋哺乳动物研究提供了显著的优势,在具有挑战性的声学环境中实现了高效和准确的行为分析.
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