Dept. Electrical & Computer Engineering, McGill University, Montreal, Canada.
Dept. Biology, McGill University, Montreal, Canada.
PLoS Comput Biol. 2021 Apr 8;17(4):e1008820. doi: 10.1371/journal.pcbi.1008820. eCollection 2021 Mar.
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.
声音信号的声学结构变化对于传递社交信息非常重要。然而，对于接收者用来解码相关社交信息的特征，我们知之甚少。在这里，我们采用了一种广泛的、自下而上的方法来描绘对处理斑马雀歌声中的社交信息可能重要的特征空间。使用操作性技术，我们发现雌性斑马雀可以持续区分来自不同社交环境的简短歌曲片段（“动机”）。然后，我们应用机器学习算法根据数千个时间序列特征对动机进行分类，并发现用于动机区分的声学特征。除了突出经典的声学特征外，该算法还揭示了用于歌曲区分的新特征，例如时间不可逆性的度量（即信号的实际和时间反转之间的统计特性的差异程度）。此外，该算法准确地预测了雌性对单个动机样本的表现。这些数据强调并扩展了广泛的时间序列表型分析应用于声学分析和社会决策的前景。
