从被动声学监测的深度音频检测网络,一个临灭绝的灵长类动物
Ziwen Hong1, Yinghong Xie2,3,4, Kai He5
1School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, China.
Integrative zoology
|November 14, 2025
概括
一个新的深度音频检测网络 (DeepADN) 准确地识别了白头从杂的声学数据中的呼叫. 这种人工智能工具增强了生物多样性监测,并显示了跨物种保护应用的潜力.
科学领域:
- 生态与保护生物学 生态与保护生物学
- 人工智能在野生动物监测中的应用
- 生物声学是一种生物声学.
背景情况:
- 生物多样性监测对于保护至关重要,它依赖于诸如物种发生率和种群动态等数据.
- 被动声学监测 (PAM) 产生了大量的野生动物保护数据集,需要先进的分析方法.
- 将人工智能 (AI) 与机器学习模型集成,解决了分析大量PAM数据的挑战,特别是隔离目标物种的发音.
研究的目的:
- 开发和评估一种基于人工智能的方法,从声学监测数据中准确检测白头的呼叫.
- 评估开发方法在减少手动数据审查工作负载方面的效率.
- 调查人工智能模型对更广泛的保护工作的跨物种适用性.
主要方法:
- 使用深度音频检测网络 (DeepADN),直接将音频信号转换为声学特征.
- 这些特征被卷积神经网络处理,用于自动检测灵长类动物的发声.
- 这种方法绕过了传统的光谱图像转换,用于直接的音频分析.
主要成果:
- DeepADN 实现了 98.22% 的高回忆率来检测白头的呼叫,即使在杂的环境中.
- 使用自动检测系统,手动审查工作量显著减少了87.07%.
- 该模型展示了成功的跨物种检测,识别了弗朗索瓦的鱼呼叫.
结论:
- DeepADN提供了一种高效准确的AI驱动解决方案,用于分析野生动物监测中的声学数据.
- 该方法显著提高了被动声学监测的功能,以保护危物种.
- 证明的跨物种适用性表明DeepADN在全球生物多样性研究和保护方面具有广泛的潜力.
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