Studying the vocalisations of wild animals can be a challenge due to the limitations of traditional computational methods, which often are time-consuming and lack reproducibility. Here, I present pykanto, a new software package that provides a set of tools to build, manage, and explore large sound databases. It can automatically find discrete units in animal vocalisations, perform semi-supervised labelling of individual repertoires with a new interactive web app, and feed data to deep learning models to study things like individual signatures and acoustic similarity between individuals and populations. To demonstrate its capabilities, I put the library to the test on the vocalisations of male great tits in Wytham Woods, near Oxford, UK. The results show that the identities of individual birds can be accurately determined from their songs and that the use of pykanto improves the efficiency and reproducibility of the process.

翻译：研究野生动物发声因传统计算方法的局限性而面临挑战，这些方法往往耗时且缺乏可重复性。本文介绍pykanto，一个提供构建、管理和探索大型声音数据库工具集的新型软件包。该软件包能自动发现动物发声中的离散单元，通过新增的交互式网页应用实现个体曲库的半监督标注，并为深度学习模型提供数据以研究个体特征标记、个体间及种群间的声学相似性等课题。为验证其性能，我们在英国牛津附近威萨姆森林雄性大山雀的发声数据上对该库进行了测试。结果表明，根据鸣声可准确判定个体身份，且使用pykanto提升了该流程的效率和可重复性。