Open audio databases such as Xeno-Canto are widely used to build datasets to explore bird song repertoire or to train models for automatic bird sound classification by deep learning algorithms. However, such databases suffer from the fact that bird sounds are weakly labelled: a species name is attributed to each audio recording without timestamps that provide the temporal localization of the bird song of interest. Manual annotations can solve this issue, but they are time consuming, expert-dependent, and cannot run on large datasets. Another solution consists in using a labelling function that automatically segments audio recordings before assigning a label to each segmented audio sample. Although labelling functions were introduced to expedite strong label assignment, their classification performance remains mostly unknown. To address this issue and reduce label noise (wrong label assignment) in large bird song datasets, we introduce a data-centric novel labelling function composed of three successive steps: 1) time-frequency sound unit segmentation, 2) feature computation for each sound unit, and 3) classification of each sound unit as bird song or noise with either an unsupervised DBSCAN algorithm or the supervised BirdNET neural network. The labelling function was optimized, validated, and tested on the songs of 44 West-Palearctic common bird species. We first showed that the segmentation of bird songs alone aggregated from 10% to 83% of label noise depending on the species. We also demonstrated that our labelling function was able to significantly reduce the initial label noise present in the dataset by up to a factor of three. Finally, we discuss different opportunities to design suitable labelling functions to build high-quality animal vocalizations with minimum expert annotation effort.

翻译：开放音频数据库（如Xeno-Canto）广泛用于构建探索鸟类鸣声曲目或通过深度学习算法训练自动鸟类声音分类模型的数据集。然而，此类数据库存在鸟类声音标签弱标注的问题：每个音频文件仅关联物种名称，缺少标注目标鸟类鸣声时间定位的时间戳。人工标注虽能解决该问题，但耗时、依赖专家经验且难以应用于大规模数据集。另一种解决方案是使用标签函数自动分割音频记录，随后为每个分割后的音频样本分配标签。尽管引入标签函数旨在加速强标签的生成，但其分类性能仍不明确。为解决这一问题并降低大型鸟类鸣声数据集的标签噪声（错误标签分配），我们提出了一种以数据为中心的新型标签函数，包含三个连续步骤：1）时频声音单元分割，2）为每个声音单元计算特征，3）通过无监督DBSCAN算法或有监督BirdNET神经网络将每个声音单元分类为鸟类鸣声或噪声。该标签函数针对44种西古北界常见鸟类物种的鸣声进行了优化、验证和测试。我们首先发现，仅对鸟类鸣声进行分割便会根据物种不同引入10%至83%的标签噪声。同时证明，我们的标签函数能将数据集中初始存在的标签噪声显著降低至多三倍。最后，我们探讨了设计合适标签函数以在最小化专家标注工作量的前提下构建高质量动物发声数据集的多种可能性。