89 citations · 156 across the 6 of their papers we have counts for
9 papers
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…
MicAugment: One-shot Microphone Style Transfer
Zalán Borsos, Yunpeng Li, Beat Gfeller +1
A crucial aspect for the successful deployment of audio-based models "in-the-wild" is the robustness to the transformations introduced by heterogeneous acquisition conditions. In t…
Sense and Learn: Self-Supervision for Omnipresent Sensors
Aaqib Saeed, Victor Ungureanu, Beat Gfeller
Learning general-purpose representations from multisensor data produced by the omnipresent sensing systems (or IoT in general) has numerous applications in diverse use cases. Exist…
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…