On the Transferability of Large-Scale Self-Supervision to Few-Shot Audio Classification
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Abstract (arXiv)
In recent years, self-supervised learning has excelled for its capacity to learn robust feature representations from unlabelled data. Networks pretrained through self-supervision serve as effective feature extractors for downstream tasks, including Few-Shot Learning. While the evaluation of unsupervised approaches for few-shot learning is well-established in imagery, it is notably absent in acoustics. This study addresses this gap by assessing large-scale self-supervised models' performance in few-shot audio classification. Additionally, we explore the relationship between a model's few-shot learning capability and other downstream task benchmarks. Our findings reveal state-of-the-art performance in some few-shot problems such as SpeechCommandsv2, as well as strong correlations between speech-based few-shot problems and various downstream audio tasks.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 20 of its 21 references that are in the map. First posted on arXiv 2 Feb 2024.
Builds on
20 of 21 references.
References · 20
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8 more
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.