3 papers
cs.SD2026
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…
cs.LG2025
SequenceLayers: Sequence Processing and Streaming Neural Networks Made Easy
RJ Skerry-Ryan, Julian Salazar, Soroosh Mariooryad +8
We introduce a neural network layer API and library for sequence modeling, designed for easy creation of sequence models that can be executed both layer-by-layer (e.g., teacher-for…
cs.CL2025
Robust and Unbounded Length Generalization in Autoregressive Transformer-Based Text-to-Speech
Eric Battenberg, RJ Skerry-Ryan, Daisy Stanton +4
Autoregressive (AR) Transformer-based sequence models are known to have difficulty generalizing to sequences longer than those seen during training. When applied to text-to-speech…