RT-SEMamba: Real-Time Speech Enhancement Mamba via Progressive Knowledge Distillation
We present RT-SEMamba, a fully causal speech enhancement (SE) model built upon causal time-frequency Mamba blocks. Unlike Transformer-based architectures that rely on a growing key-value cache, Mamba propagates a fixed-size recurrent state per layer, enabling memory- and bandwidth-efficient long-form inference. We further introduce a progressive knowledge distillation (KD) strategy that compresses an 8-layer teacher into a shallow 1-layer student by jointly distilling complex spectral outputs and intermediate representations. On Voicebank-DEMAND, the 8-layer RT-SEMamba achieves 3.32 PESQ with a 25 ms algorithmic latency constraint, and the distilled 1-layer student improves over a naive 1...
| Authors | Rong Chao, Sung-Feng Huang, Moreno La Quatra et al. |
|---|---|
| Primary category | cs.SD |
| Published | 12 August 2026 |
| Last updated at source | 16 September 2026 |
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