Automatic Bat Call Classification using Transformer Networks
Frank Fundel Daniel A. Braun Sebastian Gottwald
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.