Bird@Edge: Bird Species Recognition at the Edge-Reference-Cited by-同舟云学术

Bird@Edge: Bird Species Recognition at the Edge

Published:2022 Issue: Volume: Page:69-86
ISSN:0302-9743
Container-title:Networked Systems
language:
Short-container-title:

Author:

Höchst Jonas^ORCID,Bellafkir Hicham,Lampe Patrick^ORCID,Vogelbacher Markus,Mühling Markus^ORCID,Schneider Daniel^ORCID,Lindner Kim,Rösner Sascha^ORCID,Schabo Dana G.,Farwig Nina^ORCID,Freisleben Bernd^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-17436-0_6

Reference25 articles.

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2. Darras, K., et al.: Comparing the sampling performance of sound recorders versus point counts in bird surveys: a meta-analysis. J. Appl. Ecol. 55(6), 2575–2586 (2018). https://doi.org/10.1111/1365-2664.13229

3. Disabato, S., Canonaco, G., Flikkema, P.G., Roveri, M., Alippi, C.: Birdsong detection at the edge with deep learning. In: 2021 IEEE International Conference on Smart Computing (SMARTCOMP), pp. 9–16. IEEE (2021)

4. Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. In: 9th International Conference on Learning Representations, ICLR 2021, Austria (2021)

5. Gallacher, S., et al.: Shazam for bats: internet of things for continuous real-time biodiversity monitoring. IET Smart Cities 3(3), 171–183 (2021)

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