Suppr超能文献

非洲企鹅(斑嘴环企鹅)的发声个体特征线索:一种源-滤波器理论方法

Vocal individuality cues in the African penguin (Spheniscus demersus): a source-filter theory approach.

作者信息

Favaro Livio, Gamba Marco, Alfieri Chiara, Pessani Daniela, McElligott Alan G

机构信息

Department of Life Sciences and Systems Biology, University of Turin, Via Accademia Albertina 13, 10123 Turin, Italy.

Biological and Experimental Psychology, School of Biological and Chemical Sciences, Queen Mary University of London, Mile End Road, London E1 4NS, UK.

出版信息

Sci Rep. 2015 Nov 25;5:17255. doi: 10.1038/srep17255.

Abstract

The African penguin is a nesting seabird endemic to southern Africa. In penguins of the genus Spheniscus vocalisations are important for social recognition. However, it is not clear which acoustic features of calls can encode individual identity information. We recorded contact calls and ecstatic display songs of 12 adult birds from a captive colony. For each vocalisation, we measured 31 spectral and temporal acoustic parameters related to both source and filter components of calls. For each parameter, we calculated the Potential of Individual Coding (PIC). The acoustic parameters showing PIC ≥ 1.1 were used to perform a stepwise cross-validated discriminant function analysis (DFA). The DFA correctly classified 66.1% of the contact calls and 62.5% of display songs to the correct individual. The DFA also resulted in the further selection of 10 acoustic features for contact calls and 9 for display songs that were important for vocal individuality. Our results suggest that studying the anatomical constraints that influence nesting penguin vocalisations from a source-filter perspective, can lead to a much better understanding of the acoustic cues of individuality contained in their calls. This approach could be further extended to study and understand vocal communication in other bird species.

摘要

非洲企鹅是一种原产于非洲南部的筑巢海鸟。在环企鹅属的企鹅中,发声对于社交识别很重要。然而,尚不清楚叫声的哪些声学特征能够编码个体身份信息。我们记录了来自一个圈养群体的12只成年企鹅的联络叫声和狂喜展示歌声。对于每种发声,我们测量了与叫声的声源和滤波器成分相关的31个频谱和时间声学参数。对于每个参数,我们计算了个体编码潜力(PIC)。显示PIC≥1.1的声学参数被用于进行逐步交叉验证判别函数分析(DFA)。DFA将66.1%的联络叫声和62.5%的展示歌声正确分类到正确的个体。DFA还进一步筛选出了对联络叫声而言重要的10个声学特征以及对展示歌声而言重要的9个声学特征,这些特征对声音个体性很关键。我们的结果表明,从声源-滤波器角度研究影响筑巢企鹅发声的解剖学限制,能够让我们更好地理解其叫声中包含的个体性声学线索。这种方法可以进一步扩展到研究和理解其他鸟类的声音交流。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cd28/4658557/22f4baaa6ec3/srep17255-f1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验