InsectSound1000 一个昆虫声音数据集,用于基于深度学习的声学昆虫识别
Jelto Branding1, Dieter von Hörsten2, Elias Böckmann3
1Julius Kühn Institute (JKI), Institute for Application Techniques in Plant Protection, Braunschweig, 38104, Germany. jelto.branding@julius-kuehn.de.
Scientific data
|May 9, 2024
概括
昆虫Sound1000是一个大型的,高质量的数据集,包含来自12个物种的超过169,000个昆虫声音样本. 该资源使得使用深度学习模型开发数字昆虫传感器,用于使用深度学习模型进行自动化害虫和生态监测.
科学领域:
- 生物声学是一种生物声学.
- 机器学习 机器学习
- 生态监测 生态监测
背景情况:
- 自动化昆虫监测对于害虫和生态评估至关重要.
- 现有的声学昆虫识别系统需要大量高质量的数据集进行开发.
- 昆虫Sound1000数据集通过提供广泛的标记昆虫声音记录来满足这一需求.
研究的目的:
- 介绍InsectSound1000数据集,这是一个全面的昆虫声学录音集合.
- 促进用于声学昆虫识别的深度学习模型的培训.
- 支持开发用于监控应用的新型数字昆虫传感器.
主要方法:
- 收集了1000多个小时的录音在一个无声室.
- 使用一个四通道,低噪音测量麦克风阵列.
- 从12种昆虫物种中提取并标记了超过169,000个声音样本.
主要成果:
- 昆虫Sound1000数据集包含超过169,000个标记的声音样本.
- 录音可以捕捉到各种各样的昆虫声音水平,从大声到人类听不到的声音.
- 每个样本都是一个高分辨率的四通道波形文件.
结论:
- InsectSound1000是培训深度学习模型用于声学昆虫识别的宝贵资源.
- 该数据集可以加速数字昆虫传感器的开发,用于自动监测.
- 详细的方法允许未来的数据集扩展和调整.
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