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Detecting the presence of animal vocalisations in nature is essential to study animal populations and their behaviors.
A. Arnold, R. Nallapati, and W. W. Cohen, “A comparative study of methods for transductive transfer learning,” in Seventh IEEE international conference on data mining workshops (ICDMW 2007) . IEEE, 2007, pp. 77–82
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T. Ganchev, Computational bioacoustics: biodiversity monitoring and assessment . Walter de Gruyter GmbH & Co KG, 2017, vol. 4
2017
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J. Snell, K. Swersky, and R. S. Zemel, “Prototypical networks for few-shot learning,” 2017
2017
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P. E. Caiger, M. J. Dean, A. I. DeAngelis, L. T. Hatch, A. N. Rice, J. A. Stanley, C. Tholke, D. R. Zemeckis, and S. M. Van Parijs, “A decade of monitoring atlantic cod gadus morhua spawning aggregations in massachusetts bay using passive acoustics,” Marine Ecology Progress Series , vol. 635, pp. 89–103, 2020
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2020
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S. Gillings and C. Scott, “Nocturnal flight calling behaviour of thrushes in relation to artificial light at night,” Ibis , vol. 163, no. 4, pp. 1379–1393, 2021
2021
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V. Morfi, I. Nolasco, V. Lostanlen, S. Singh, A. Strandburg-Peshkin, D. Benvent, and D. Stowell, “Few-shot bioacoustic event detection: A new task at the dcase 2021 challenge.”
2021
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D. Tuia, B. Kellenberger, S. Beery, B. R. Costelloe, S. Zuffi, B. Risse, A. Mathis, M. W. Mathis, F. Van Langevelde, T. Burghardt et al. , “Perspectives in machine learning for wildlife conservation,” Nature communications , vol. 13, no. 1, pp. 1–15, 2022
2022
Cited alongside, same era.
D. Stowell, “Computational bioacoustics with deep learning: a review and roadmap,” PeerJ , vol. 10, p. e13152, 2022
2022
Cited alongside, same era.
R. Li, J. Liang, and H. Phan, “Few-shot bioacoustic event detection: Enhanced classifiers for prototypical networks,” in Proceedings of the 7th Detection and Classification of Acoustic Scenes and Events 2022 Workshop (DCASE2022) , Nancy, France, November 2022
2022
Cited alongside, same era.
J. Liang, H. Phan, and E. Benetos, “Leveraging label hierachies for few-shot everyday sound recognition,” in Proceedings of the 7th Detection and Classification of Acoustic Scenes and Events 2022 Workshop (DCASE2022) , Nancy, France, November 2022
2022
2023
Later among the works it cites.
M. Boudiaf, T. Denton, B. Van Merriënboer, V. Dumoulin, and E. Triantafillou, “In search for a generalizable method for source free domain adaptation,” in International Conference on Machine Learning . PMLR, 2023, pp. 2914–2931
2023
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I. Nolasco, S. Singh, V. Morfi, V. Lostanlen, A. Strandburg-Peshkin, E. Vidaña-Vila, L. Gill, H. Pamuła, H. Whitehead, I. Kiskin et al. , “Learning to detect an animal sound from five examples,” Ecological informatics , vol. 77, p. 102258, 2023
2023
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J. Liang, X. Liu, H. Liu, H. Phan, E. Benetos, M. D. Plumbley, and W. Wang, “Adapting Language-Audio Models as Few-Shot Audio Learners,” in Proc. INTERSPEECH 2023 , 2023, pp. 276–280
2023
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Cited alongside, same era.
2022
Cited alongside, same era.
H. Liu, X. Liu, X. Mei, Q. Kong, W. Wang, and M. D. Plumbley, “Surrey system for dcase 2022 task 5 : Few-shot bioacoustic event detection with segment-level metric learning,” DCASE2022 Challenge Technical Report, Tech. Rep., 2022
2022
Cited alongside, same era.
B. Ghani, T. Denton, S. Kahl, and H. Klinck, “Global birdsong embeddings enable superior transfer learning for bioacoustic classification,” Scientific Reports , vol. 13, no. 1, p. 22876, 2023
2023
Cited alongside, same era.
Later among the works it cites.
2023
Later among the works it cites.
J. Liang, H. Phan, and E. Benetos, “Learning from taxonomy: Multi-label few-shot classification for everyday sound recognition,” in ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2024, pp. 1–5
2024
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