Feb. 12, 2024, 5:43 a.m. | Ilyass Moummad Romain Serizel Nicolas Farrugia

cs.LG updates on arXiv.org arxiv.org

Self-supervised learning (SSL) in audio holds significant potential across various domains, particularly in situations where abundant, unlabeled data is readily available at no cost. This is pertinent in bioacoustics, where biologists routinely collect extensive sound datasets from the natural environment. In this study, we demonstrate that SSL is capable of acquiring meaningful representations of bird sounds from audio recordings without the need for annotations. Our experiments showcase that these learned representations exhibit the capacity to generalize to new bird species …

audio bird classification cost cs.lg cs.sd data datasets domains eess.as environment few-shot natural self-supervised learning sound ssl study supervised learning

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