波尔内亚猩猩的长呼声中的声乐复杂性
Wendy M Erb1,2, Whitney Ross1, Haley Kazanecki1
1K. Lisa Yang Center for Conservation Bioacoustics, Cornell Lab of Ornithology, Cornell University, Ithaca, NY, United States of America.
PeerJ
|May 20, 2024
概括
婆罗洲猩猩的长呼是复杂的,原因是连续的声音分级,而不是离散的呼叫类型. 机器学习揭示了分类发音的挑战,但更新的方法提高了准确性和可重现性.
科学领域:
- 动物沟通 动物沟通
- 生物声学是一种生物声学.
- 进化生物学是进化的生物学.
背景情况:
- 声音的复杂性是理解动物交流和进化的关键.
- 量化语音复杂性,特别是在分级发音中,带来了重大挑战.
- 男子婆罗洲猩猩产生复杂的长呼声,具有可变的声音类型.
研究的目的:
- 为了评估在分类猩猩长呼叫类型的观察者之间的可靠性.
- 使用机器学习区分呼叫类型并评估它们的离散性.
- 开发一种更强大的方法来分类猩猩的声音.
主要方法:
- 对13只婆罗洲猩猩长期通话的分析.
- 利用了46个声学特征和机器学习算法 (SVM,亲和力传播,模糊的c-means).
- 用于可视化的统一多边近似和投影 (UMAP).
主要成果:
- 监督分类显示观察者之间的可靠性低,准确性差,表明非离散的呼叫类型.
- 使用支矢量机的更新脉冲分类方法显示出高可重现性和准确性.
- 连续的声音分级,而不是不同的呼叫类型,似乎驱动声乐的复杂性.
结论:
- 猩猩长呼声通过声音的连续分级表现出复杂性,挑战离散类型分类.
- 开发的分类方法为研究分级声系统提供了更高的可靠性和准确性.
- 这项研究为定义呼叫类型和评估动物发声的分级性提供了定量方法.
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