Department of Psychology, P-217 University of Alberta, Edmonton, Alberta, T6G 2R3, Canada.
J Acoust Soc Am. 2021 Oct;150(4):3038. doi: 10.1121/10.0006532.
In songbirds, song has traditionally been considered a vocalization mainly produced by males. However, recent research suggests that both sexes produce song. While the function and structure of male black-capped chickadee (Poecile atricapillus) fee-bee song have been well-studied, research on female song is comparatively limited. Past discrimination and playback studies have shown that male black-capped chickadees can discriminate between individual males via their fee-bee songs. Recently, we have shown that male and female black-capped chickadees can identify individual females via their fee-bee song even when presented with only the bee position of the song. Our results using discriminant function analyses (DFA) support that female songs are individually distinctive. We found that songs could be correctly classified to the individual (81%) and season (97%) based on several acoustic features including but not limited to bee-note duration and fee-note peak frequency. In addition, an artificial neural network was trained to identify individuals based on the selected DFA acoustic features and was able to achieve 90% accuracy by individual and 93% by season. While this study provides a quantitative description of the acoustic structure of female song, the perception and function of female song in this species requires further investigation.
在鸣禽中，传统上认为歌曲主要是由雄性产生的发声行为。然而，最近的研究表明，雌雄两性都能产生歌曲。尽管雄性黑顶山雀（Poecile atricapillus）的 fee-bee 歌曲的功能和结构已经得到了很好的研究，但对雌性歌曲的研究相对较少。过去的辨别和回放研究表明，雄性黑顶山雀可以通过 fee-bee 歌曲辨别个体雄性。最近，我们发现，雄性和雌性黑顶山雀甚至可以通过歌曲的 bee 位置识别个体雌性。我们使用判别函数分析（DFA）的结果支持雌性歌曲具有个体独特性。我们发现，根据 bee-note 持续时间和 fee-note 峰值频率等几个声学特征，歌曲可以正确地分类到个体（81%）和季节（97%）。此外，人工神经网络根据选定的 DFA 声学特征进行个体识别训练，能够达到个体 90%的准确率和季节 93%的准确率。虽然这项研究提供了对雌性歌曲声学结构的定量描述，但这种物种中雌性歌曲的感知和功能仍需要进一步研究。
