在山地森林中使用深度学习方法对鸟类 (Aves) 的声学检测数据集
Shih-Hung Wu1,2, Jerome Chie-Jen Ko2,3, Ruey-Shing Lin2
1Department of Biological Sciences, National Sun Yat-Sen University, Kaohsiung, Taiwan Department of Biological Sciences, National Sun Yat-Sen University Kaohsiung Taiwan.
Biodiversity data journal
|February 8, 2024
概括
这项研究介绍了台湾玉山国家公园的鸟类发声的第一个开放访问数据集,该数据集是使用人工智能识别的. 这种宝贵的资源有助于了解山林生物多样性和气候变化影响.
科学领域:
- 生态生态学 生态生态学
- 生物声学是一种生物声学.
- 人工智能的人工智能
背景情况:
- 长期监测山林野生动物对于了解人口趋势和环境影响至关重要.
- 台湾的玉山国家公园 (YSNP) 正在实施一项使用被动声学监测 (PAM) 的长期生物多样性监测项目.
- 像SILIC这样的自动声音识别模型被用来分析大量的野生动物声音.
研究的目的:
- 建立第一个使用人工智能从音景记录中获得的物种发生数据的开放访问数据集.
- 为YSNP的资源管理和决策提供必要的数据.
- 支持关于气候变化对动物分布和行为的影响的区域和全球研究.
主要方法:
- 2020年1月,在YSNP启动了一项长期生物多样性监测项目,利用六个PAM站.
- 通过使用SILIC自动化野生动物声音识别模型分析了2020-2021年的170多万个一分钟记录.
- 通过人工智能驱动的分析,从七种鸟类中提取了6,243,820个发声,生成了802,670个发声记录.
主要成果:
- 人工智能模型实现了0.95的精度和0.48到0.80的回忆,优先考虑精度以最大限度地减少假阳性.
- 数据集包含发生记录,总结为每种物种每次记录的发声数.
- 这一数据集代表了对物种发生数据收集的新方法,计划每年更新,包括更多的物种和种类.
结论:
- 这一人工智能生成的数据集在生物声学监测和生物多样性数据收集方面取得了重大进展.
- 这些发现有助于填补山林生态系统中鸟类时间活动模式的数据缺口.
- 该数据集预计将对研究气候变化对野生动物的影响以及为保护战略提供信息具有宝贵的价值.
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