使用人工智能 (BirdNET,Cry-Wolf和BioLingual) 对狼的生物声学检测
Johanne Holm Jacobsen1, Pietro Orlando2, Line Østergaard Jensen1
1Department of Chemistry and Bioscience, Aalborg University, 9220 Aalborg, Denmark.
Animals : an open access journal from MDPI
|January 28, 2026
概括
人工智能 (AI) 方法可以有效地从音频录音中检测狼的叫声,大大帮助了狼群监测. 结合人工智能的方法实现了96.2%的回忆,补充了传统的保护方法.
科学领域:
- 野生动物生物学 野生动物生物学
- 生物声学是一种生物声学.
- 在生态学中的人工智能.
背景情况:
- 狼 (Canis lupus) 种群的增加需要先进的监测技术.
- 传统的狼监测方法耗费大量资源,对于目前的种群规模来说往往是不够的.
研究的目的:
- 评估人工智能 (AI) 方法来检测和分类来自声学录音的狼叫声.
- 与传统方法相比,评估人工智能在改善狼群监测方面的有效性.
主要方法:
- 三种AI模型 (BirdNET,Cry-Wolf,BioLingual) 在来自丹麦的声学数据上进行了测试.
- 使用歌曲计SM4 (SM4) 音频录音机收集数据.
- 手动验证确定了260个狼的基本真相.
主要成果:
- 个别的人工智能模型的回忆和精度各不相同,其中BirdNET的回忆率最高 (78.5%).
- 人工智能解决方案显著减少了处理时间,提供了显著的效率提升.
- 结合人工智能方法实现了96.2%的高回忆率 (250/260个叫声被检测到).
结论:
- 人工智能方法虽然不是完全自主,但作为强大的人类辅助数据减少工具.
- 人工智能为传统的狼监测提供了一个可扩展的,非侵入性的补充.
- 人工智能提高了大规模狼群研究和保护工作的可行性.
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