这是谁的电话? 优化源标识从大黄蜂声调与等级机器学习分类器的源标识
Nikhil Phaniraj1,2,3, Kaja Wierucka1,4, Yvonne Zürcher1
1Institute of Evolutionary Anthropology (IEA), University of Zurich, Winterthurerstrasse 190, 8057 Zürich, Switzerland.
Journal of the Royal Society, Interface
|October 17, 2023
概括
研究人员开发了一种机器学习管道,通过它们的发声精确识别单个海. 这种工具有助于研究这些灵长类动物的社会沟通和语言演变.
科学领域:
- 灵长类动物的沟通方式
- 生物声学是一种生物声学.
- 在动物行为中的机器学习.
背景情况:
- 由于它们的社会性质和复杂的发音,海豚是理解声部沟通和语言演变的关键模型.
- 目前关于大黄蜂声调的研究往往依赖于二极交互或回放研究,限制了对群体级别沟通动态的理解.
- 准确识别单个发音的来源对于研究复杂的社会沟通至关重要.
研究的目的:
- 开发和优化机器学习管道,以准确识别群体环境中的鸟声调的源.
- 为了能够研究小组级沟通动态的马尔莫塞特.
- 为分析其他物种的发声提供适用于分析其他物种发声的工具.
主要方法:
- 开发一条机器学习管道,用于特征提取,选择和监督分类鱼声调.
- 实现一个层次化的机器学习算法,首先确定发声者的性别,然后缩小个体范围,最后确定来源.
- 在不同的呼叫类型和样本大小中测试管道的准确性和稳定性,包括一个数据集,其中最多有18只海.
主要成果:
- 在识别个人来源时的高准确率 (87.21%-94.42%取决于呼叫类型).
- 当从数据集中排除双胞胎时,识别准确度提高到97.79%.
- 在不同的样本大小中证明识别方法的稳定性.
结论:
- 开发的机器学习管道是一个非常有效的工具,用于精确识别松鼠声音的来源.
- 这种方法有助于研究鱼群中复杂的,群体级的语音交流.
- 管道显示了各种物种在生物声学研究中广泛应用的潜力.
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