Behavioral discrimination and time-series phenotyping of birdsong performance

Variation in the acoustic structure of vocal signals is important to communicate social information. However, relatively little is known about the features that receivers extract to decipher relevant social information. Here, we took an expansive, bottom-up approach to delineate the feature space that could be important for processing social information in zebra finch song. Using operant techniques, we discovered that female zebra finches can consistently discriminate brief song phrases (“motifs”) from different social contexts. We then applied machine learning algorithms to classify motifs based on thousands of time-series features and to uncover acoustic features for motif discrimination. In addition to highlighting classic acoustic features, the resulting algorithm revealed novel features for song discrimination, for example, measures of time irreversibility (i.e., the degree to which the statistical properties of the actual and time-reversed signal differ). Moreover, the algorithm accurately predicted female performance on individual motif exemplars. These data underscore and expand the promise of broad time-series phenotyping to acoustic analyses and social decision-making.发声信号的声学结构变异，对于社会信息的传递具有重要意义。然而，目前学界对接收者为解码相关社会信息所提取的特征仍知之甚少。本研究采用大规模自下而上的研究方法，旨在明确斑胸草雀鸣曲中处理社会信息可能涉及的特征空间。借助操作性条件反射技术，我们发现雌性斑胸草雀可稳定区分来自不同社会情境的简短鸣唱片段（motifs）。随后，我们通过机器学习算法，基于数千个时间序列特征对鸣唱片段进行分类，并挖掘出用于区分鸣唱片段的声学特征。除经典声学特征外，该算法还揭示了用于鸣唱区分的全新特征，例如时间不可逆性——即实际信号与时间反转信号的统计特性差异程度——的量化指标。此外，该算法可准确预测雌性斑胸草雀对单个鸣唱片段样本的识别表现。本研究结果进一步凸显并拓展了将大范围时间序列表型分析应用于声学研究与社会决策领域的前景。