Department for the Ecology of Animal Societies, Max Planck Institute of Animal Behavior, Constance, Germany.
Department of Biology, University of Konstanz, Constance, Germany.
J Anim Ecol. 2022 Aug;91(8):1567-1581. doi: 10.1111/1365-2656.13754. Epub 2022 Jun 9.
The manual detection, analysis and classification of animal vocalizations in acoustic recordings is laborious and requires expert knowledge. Hence, there is a need for objective, generalizable methods that detect underlying patterns in these data, categorize sounds into distinct groups and quantify similarities between them. Among all computational methods that have been proposed to accomplish this, neighbourhood-based dimensionality reduction of spectrograms to produce a latent space representation of calls stands out for its conceptual simplicity and effectiveness. Goal of the study/what was done: Using a dataset of manually annotated meerkat Suricata suricatta vocalizations, we demonstrate how this method can be used to obtain meaningful latent space representations that reflect the established taxonomy of call types. We analyse strengths and weaknesses of the proposed approach, give recommendations for its usage and show application examples, such as the classification of ambiguous calls and the detection of mislabelled calls. What this means: All analyses are accompanied by example code to help researchers realize the potential of this method for the study of animal vocalizations.
在声学记录中手动检测、分析和分类动物叫声既费力又需要专业知识。因此，需要客观、可推广的方法来检测这些数据中的潜在模式，将声音分为不同的类别，并量化它们之间的相似性。在所有被提出的用于实现这一目标的计算方法中，基于邻域的声谱图降维方法因其概念简单和有效性而脱颖而出，可生成叫声的潜在空间表示。
研究目的/所做工作：使用经过手动标注的猫鼬 Suricata suricatta 叫声数据集，我们演示了如何使用这种方法获得有意义的潜在空间表示，反映已建立的叫声类型分类法。我们分析了所提出方法的优缺点，为其使用提供了建议，并展示了应用示例，如模糊叫声的分类和误标记叫声的检测。
所有分析都附有示例代码，以帮助研究人员认识到该方法在动物叫声研究中的潜力。
