pykanto: a python library to accelerate research on wild bird song

Nilo Merino Recalde

 University of Oxford

pykanto：一个加速野生鸟类歌曲研究的python库

要点：

 1.由于传统计算方法的局限性，研究野生动物的发声可能是一项挑战，因为传统计算方法往往耗时且缺乏再现性。

 2.在这里，介绍了pykanto，这是一个新的软件包，它提供了一组工具来构建、管理和探索大型声音数据库。它可以自动找到动物发声中的离散单元，使用新的交互式网络应用程序对个体曲目进行半监督标记，并将数据输入深度学习模型，以研究个体签名和个体与群体之间的声学相似性等问题。

一句话总结：

为了证明它的能力，我在英国牛津附近的威瑟姆森林（Wytham Woods）对该库进行了雄性大山雀的发声测试。结果表明，可以从它们的歌声中准确地确定个体鸟类的身份，使用pykanto提高了这一过程的效率和再现性。[机器翻译+人工校对]

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.

https://arxiv.org/ftp/arxiv/papers/2302/2302.10340.pdf

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