3 papers
eess.AS2026
SE-MSB: End-to-End Unpaired Speech Enhancement using Mamba Schrödinger Bridges
Andreas Bagge, Andreas Nymand, Michael Riis Andersen +1
Speech enhancement (SE) models typically rely on supervised learning with paired data examples where clean speech is synthetically degraded. This paradigm limits performance in rea…
cs.LG2025
Knowing When to Quit: Probabilistic Early Exits for Speech Separation
Kenny Falkær Olsen, Mads Østergaard, Karl Ulbæk +4
In recent years, deep learning-based single-channel speech separation has improved considerably, in large part driven by increasingly compute- and parameter-efficient neural networ…
cs.SD2024
SepMamba: State-space models for speaker separation using Mamba
Thor Højhus Avenstrup, Boldizsár Elek, István László Mádi +4
Deep learning-based single-channel speaker separation has improved significantly in recent years largely due to the introduction of the transformer-based attention mechanism. Howev…