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Who calls the shots? Rethinking Few-Shot Learning for Audio
[article]
2021
arXiv
pre-print
Few-shot learning aims to train models that can recognize novel classes given just a handful of labeled examples, known as the support set. While the field has seen notable advances in recent years, they have often focused on multi-class image classification. Audio, in contrast, is often multi-label due to overlapping sounds, resulting in unique properties such as polyphony and signal-to-noise ratios (SNR). This leads to unanswered questions concerning the impact such audio properties may have
arXiv:2110.09600v1
fatcat:rj3475lmsvgv7p6hmvyazdo45e