Bird Species Recognition in Soundscapes with Self-supervised Pre-training

Author:

Bellafkir Hicham,Vogelbacher Markus,Schneider Daniel,Kizik Valeryia,Mühling Markus,Freisleben Bernd

Publisher

Springer Nature Switzerland

Reference38 articles.

1. Cohen, Y., Nicholson, D.A., Sanchioni, A., Mallaber, E.K., Skidanova, V., Gardner, T.J.: Automated annotation of birdsong with a neural network that segments spectrograms. eLife 11, e63853 (2022). https://doi.org/10.7554/eLife.63853

2. Conde, M.V., Choi, U.: Few-shot long-tailed bird audio recognition. In: Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum, Bologna, Italy. CEUR Workshop Proceedings, vol. 3180, pp. 2036–2046. CEUR-WS.org (2022). http://ceur-ws.org/Vol-3180/paper-161.pdf

3. Dai, W., Dai, C., Qu, S., Li, J., Das, S.: Very deep convolutional neural networks for raw waveforms. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 421–425 (2017). https://doi.org/10.1109/ICASSP.2017.7952190

4. Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. In: 9th Int. Conference on Learning Representations (ICLR), Austria (2021). https://openreview.net/forum?id=YicbFdNTTy

5. Gemmeke, J.F., et al.: Audio set: an ontology and human-labeled dataset for audio events. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 776–780 (2017). https://doi.org/10.1109/ICASSP.2017.7952261

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