Automatically identifying bat species from their echolocation calls is a
difficult but important task for monitoring bats and the ecosystem they live
in. Major challenges in automatic bat call identification are high call
variability, similarities between species, interfering calls and lack of
annotated data. Many currently available models suffer from relatively poor
performance on real-life data due to being trained on single call datasets and,
moreover, are often too slow for real-time classification. Here, we propose a
Transformer architecture for multi-label classification with potential
applications in real-time classification scenarios. We train our model on
synthetically generated multi-species recordings by merging multiple bats calls
into a single recording with multiple simultaneous calls. Our approach achieves
a single species accuracy of 88.92% (F1-score of 84.23%) and a multi species
macro F1-score of 74.40% on our test set. In comparison to three other tools on
the independent and publicly available dataset ChiroVox, our model achieves at
least 25.82% better accuracy for single species classification and at least
6.9% better macro F1-score for multi species classification.

通过合成多物种录音并将多种蝙蝠呼叫合并到一个录音中，我们提出了一种变压器架构用于多标签分类，该方法在实时分类场景中具有潜在应用，单一物种准确率达到 88.92%（F1 分数 84.23%），多物种宏 F1 分数达到 74.40%。相比于三个其他工具在独立和公开可用的数据集 ChiroVox 上，我们的模型单一物种分类准确率至少提高 25.82%，多物种分类宏 F1 分数至少提高 6.9%。