Bachelor Thesis, graded 1.0 (best grade)
Automatically identifying bat species from their echolocation calls is a difficult but crucial task for monitoring bats and the ecosystem they live in. The main issues are high call variability, similarities between species, interfering calls and lack of annotated data. This thesis proposes a deep learning approach that attempts to tackle these issues by using a Transformer-hybrid architecture that utilizes temporal information and artificially generated interfering calls for multi-label classification. Our method is more efficient than previous methods and has potential for applications in real-time classification scenarios. We were able to achieve 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. We compared our method to three other tools on an independent and publicly available dataset, which showed that our method achieved at least 25.82% better accuracy for single species classification and at least 6.9% better macro F1-score for multi species classification. We created a web-demo version with visualization for the multi-label classification and example files on https://bat.hadros.de/. We also created a command-line tool for fast inference on large amounts of data. The entirety of the implementation is opensource.