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Buzzfindr:自动检测蝙蝠回声定位记录中的声

Joël W Jameson¹

¹WSP, Montreal, Quebec, Canada.

|August 20, 2024

概括

现在可以使用随机森林算法高精度地实现蝙蝠食声的自动检测. 这一进步有助于识别关键的蝙蝠食息地,用于保护工作.

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.
保护生物学保护生物学

背景情况:

声学蝙蝠调查对于野生动物监测至关重要,但分析往往缺乏息地功能洞察力.
食声,表明食成功,是评估息地质量的关键.

研究的目的:

在声学录音中自动识别蝙蝠食的声.
开发一种可靠的方法来评估蝙蝠食息地质量.

主要方法:

使用"生物声学"R包从蝙蝠录音中检测信号.
提取了时间和信号强度参数来描述养声.
采用人工神经网络和随机森林算法进行分类,选择随机森林进行假阳性控制.

主要成果:

随机森林模型,名为buzzfindr,在检测食声方面实现了93.4%的整体准确性.
检测准确度在不同的记录设备和蝙蝠物种频率组中是一致的.
关键的声特征包括脉冲间隔标准偏差,平均脉冲持续时间和信号与噪声比.

结论:

自动食声检测显著提高了声学蝙蝠研究的分析工作流程.
这项技术改善了对蝙蝠息地使用和质量的推断,支持保护战略.

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相关概念视频

Echo

Echo

The human ear cannot distinguish between two sources of sound if they happen to reach within a specific time interval, typically 0.1 seconds apart. More than this, and they are perceived as separate sources.
Imagine the sound is reflected back to the ears. Assuming that the source is very close to the human, the difference between hearing the two sounds—the emitted sound and the reflected sound—may be more than the minimum time for perceiving distinct sounds. If this is the case,...