Abstract
Biodiversity is important for several ecosystem services that provide the existential basis for human life. The current decline in biodiversity requires a transformation from manual, periodic assessment to automatic real-time biodiversity monitoring. Bats as one of the most widespread species among terrestrial mammals serve as important bioindicators for the health of ecosystems. Typically, bats are monitored by recording and analyzing their echolocation calls. In this paper, we present a novel approach for detecting bat echolocation calls and recognizing bat species in audio spectrograms. It is based on a transformer neural network architecture and relies on self-attention. Our experiments show that our approach outperforms state-of-the-art approaches for bat echolocation call detection and species recognition on several publicly available data sets. While our bat echolocation call detection approach achieves a performance of up to 90.2% in terms of average precision, our bat species recognition model obtains up to 88.7% accuracy for 14 bat classes occurring in Germany, some of which are difficult to distinguish even for human experts.
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Acknowledgement
This work is funded by the Hessian State Ministry for Higher Education, Research and the Arts (HMWK) (LOEWE Natur 4.0 and hessian.AI Connectom AI4Bats) and the German Academic Exchange Service (DAAD) (Transformation Partnership Program; Project OLIVIA).
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Bellafkir, H. et al. (2022). Bat Echolocation Call Detection and Species Recognition by Transformers with Self-attention. In: Bennour, A., Ensari, T., Kessentini, Y., Eom, S. (eds) Intelligent Systems and Pattern Recognition. ISPR 2022. Communications in Computer and Information Science, vol 1589. Springer, Cham. https://doi.org/10.1007/978-3-031-08277-1_16
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