改进大加勒比海海在整个息地使用神经网络检测大加勒比海的发声
Eric A Ramos1,2, Amit Galor3, Michael Faran3
1Mote Marine Laboratory, Sarasota, Florida, United States of America.
PloS one
|February 13, 2026
概括
这项研究使用神经网络来检测和分类大加勒比海 (Trichechus manatus manatus) 在杂的水下环境中的发声. 人工智能模型实现了高准确度,有助于鱼保护工作.
科学领域:
- 海洋生物学 海洋生物学
- 生物声学是一种生物声学.
- 人工智能的人工智能
背景情况:
- 水下声学环境对检测和分类海洋哺乳动物的声音构成挑战.
- 准确识别大加勒比海海 (Trichechus manatus manatus) 的呼叫对于行为研究和保护至关重要.
研究的目的:
- 探索神经网络的有效性,用于自动检测和分类海豚声调.
- 开发一个强大的AI模型,用于复杂的海洋声景中识别海叫声.
主要方法:
- 一个卷积神经网络 (CNN) 架构被训练在一个大数据集的海声调.
- 使用先进的信号处理 (过,规范化) 和数据增强.
- 功能提取侧重于相关的声学特征.
主要成果:
- 美国有线电视新闻网在Wildtracks数据集上获得了95.6%的F1分数.
- 在最小微调后,在Placencia数据集上获得了64.4%的F1得分,证明了概括能力.
- 该模型在识别海叫声方面表现出有效性,尽管噪音水平各不相同.
结论:
- 神经网络为自动化鱼发声检测和分类提供了一个有希望的进步.
- 这项技术可以加强人口监测,并为大加勒比海海的保护战略提供信息.
- 为了更广泛的应用,需要进一步改进模型通用化.
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