We present a robust classification approach for avian vocalization in complex and diverse soundscapes, achieving second place in the BirdCLEF2021 challenge. We illustrate how to make full use of pre-trained convolutional neural networks, by using an efficient modeling and training routine supplemented by novel augmentation methods. Thereby, we improve the generalization of weakly labeled crowd-sourced data to productive data collected by autonomous recording units. As such, we illustrate how to progress towards an accurate automated assessment of avian population which would enable global biodiversity monitoring at scale, impossible by manual annotation.

中文翻译：

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在弱监督下识别不同声景中的鸟类

我们为复杂多样的音景中的鸟类发声提供了一种强大的分类方法，在 BirdCLEF2021 挑战中获得第二名。我们通过使用有效的建模和训练程序并辅以新颖的增强方法来说明如何充分利用预训练的卷积神经网络。因此，我们改进了弱标记的众包数据对自主记录单元收集的生产数据的泛化。因此，我们说明了如何对鸟类种群进行准确的自动评估，这将实现大规模的全球生物多样性监测，而手动注释是不可能的。