89 citations · 156 across the 6 of their papers we have counts for
5 papers · 1 filter
Text-Driven Separation of Arbitrary Sounds
Kevin Kilgour, Beat Gfeller, Qingqing Huang +3
We propose a method of separating a desired sound source from a single-channel mixture, based on either a textual description or a short audio sample of the target source. This is…
One-shot conditional audio filtering of arbitrary sounds
Beat Gfeller, Dominik Roblek, Marco Tagliasacchi
We consider the problem of separating a particular sound source from a single-channel mixture, based on only a short sample of the target source. Using SoundFilter, a wave-to-wave…
Learning to Denoise Historical Music
Yunpeng Li, Beat Gfeller, Marco Tagliasacchi +1
We propose an audio-to-audio neural network model that learns to denoise old music recordings. Our model internally converts its input into a time-frequency representation by means…
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…
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…