1 citations · 3 across the 4 of their papers we have counts for
4 papers
SURF: Separation via Unsupervised Remixing Flow
Henry Li, Robin Scheibler, Efthymios Tzinis +3
The goal of single-channel source separation is to reconstruct sources given their mixture. In supervised settings where vast amounts of clean source data are available, this c…
I-Con: A Unifying Framework for Representation Learning
Shaden Alshammari, John Hershey, Axel Feldmann +2
As the field of representation learning grows, there has been a proliferation of different loss functions to solve different classes of problems. We introduce a single information-…
Separating the "Chirp" from the "Chat": Self-supervised Visual Grounding of Sound and Language
Mark Hamilton, Andrew Zisserman, John R. Hershey +1
We present DenseAV, a novel dual encoder grounding architecture that learns high-resolution, semantically meaningful, and audio-visually aligned features solely through watching vi…
Unsupervised Improved MVDR Beamforming for Sound Enhancement
Jacob Kealey, John Hershey, François Grondin
Neural networks have recently become the dominant approach to sound separation. Their good performance relies on large datasets of isolated recordings. For speech and music, isolat…