89 citations · 156 across the 6 of their papers we have counts for
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eess.AS2019★ 89 cited
SPICE: Self-supervised Pitch Estimation
Beat Gfeller, Christian Frank, Dominik Roblek +3
We propose a model to estimate the fundamental frequency in monophonic audio, often referred to as pitch estimation. We acknowledge the fact that obtaining ground truth annotations…
eess.AS2019★ 27 cited
Self-supervised audio representation learning for mobile devices
Marco Tagliasacchi, Beat Gfeller, Félix de Chaumont Quitry +1
We explore self-supervised models that can be potentially deployed on mobile devices to learn general purpose audio representations. Specifically, we propose methods that exploit t…