pykanto：一款加速野生鸟类鸣唱研究的Python库
pykanto: a python library to accelerate research on wild bird song
AI总结：
针对野生动物发声研究传统方法效率低、可复现性差的问题，本文提出Python库pykanto，提供声库构建、半监督标注、深度学习数据供给等功能，经大山雀数据验证可高效准确识别个体鸣唱，提升研究效率与可复现性。
摘要：
由于传统计算方法存在耗时长、可复现性差的局限，研究野生动物的发声行为颇具挑战。本文推出全新软件包pykanto，它提供一系列工具用于构建、管理和探索大型声音数据库。该库可自动识别动物发声中的离散单元，通过一款新的交互式网页应用对个体鸣唱曲目进行半监督标注，还能将数据输入深度学习模型，用于研究个体特征标记、个体与种群间的声学相似性等问题。为验证其性能，研究者用英国牛津附近Wytham Woods的雄性大山雀发声数据对该库进行了测试。结果表明，可通过鸣唱准确识别鸟类个体身份，且pykanto的使用提升了研究流程的效率与可复现性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
