中国竹的声乐活动模式使用BirdNET分析仪
Jinjuan Mei1, Lingna Li1,2, Wenwen Zhang1,3
1State Environmental Protection Key Laboratory on Biodiversity and Biosafety, Research Center for Biodiversity Conservation and Biosafety, Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, China.
Animals : an open access journal from MDPI
|January 28, 2026
概括
使用BirdNET的被动声学监测 (PAM) 显示了识别中国竹声调的局限性. 一个改进的随机森林 (RF) 模型显著提高了这种特有物种的识别准确性.
科学领域:
- 鸟类学 鸟类学是一门学科.
- 生物声学是一种生物声学.
- 生态学中的人工智能
背景情况:
- 被动声学监测 (PAM) 提供非侵入性,长期的鸟类声音活动跟踪.
- 在大型声学数据集中的自动数据识别存在重大挑战.
- 鸟网 (BirdNET) 是一种广泛使用的,用于物种识别的免费声音分析算法.
研究的目的:
- 评估BirdNET在识别中国竹的发音方面的有效性.
- 开发一种使用BirdNET输出进行物种识别的改进方法.
- 描述中国竹的日间和季节性呼叫模式.
主要方法:
- 评估了BirdNET-Analyzer和BirdNET-Analyzer-XHS用于物种识别的性能.
- 开发和实施一个随机森林 (RF) 模型,利用BirdNET检测结果.
- 分析了声学数据,以确定白天和季节性发声模式.
主要成果:
- 鸟网分析仪实现了16.6%的召回率;鸟网分析仪XHS实现了50.8%的精度.
- 射频模型表现出更好的性能,75.2%的回忆率和74.4%的精度.
- 中国竹表现出双模日间 (日出/日落峰值) 和单模季节性 (四月/五月峰值) 呼叫活动.
结论:
- 单独的BirdNET对于识别特定区域物种 (如中国竹) 的准确性有限.
- 拟议的射频模型显著提高了基于声学数据的自动物种识别的准确性.
- 这项研究提供了一个切实可行的框架,用于改进使用人工智能的区域物种声调识别.
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