SE-MSB: End-to-End Unpaired Speech Enhancement using Mamba Schrödinger Bridges
Speech enhancement (SE) models typically rely on supervised learning with paired data examples where clean speech is synthetically degraded. This paradigm limits performance in real-world scenarios where the target environment's specific acoustic characteristics are unknown. We propose a fully unpaired SE framework that uses principled Diffusion Schrödinger Bridges (DSB) to learn a stochastic transport process between a clean and a degraded speech distribution. Algorithms for learning transport maps are computationally heavy since they require simulating differential equations during training, usually at each training step. Therefore, we propose using a high-efficiency Mamba Diffusion Mod...
| Authors | Andreas Bagge, Andreas Nymand, Michael Riis Andersen et al. |
|---|---|
| Primary category | eess.AS |
| Published | 22 September 2026 |
| Last updated at source | 22 September 2026 |
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