SG-Mamba: Sparse Graph-Guided Mamba for Audio-Visual Speech Enhancement
Lightweight audio-visual speech enhancement (AVSE) models face a critical trade-off between computational efficiency and cross-modal alignment accuracy. While simple concatenation lacks relational expressiveness, dense cross-attention incurs computational overhead and is prone to unreliable cross-modal correspondence under strong acoustic interference. We propose Sparse Graph-Guided Mamba (SG-Mamba), a lightweight AVSE framework that integrates a sparse heterogeneous graph with a linear-complexity Mamba backbone. The graph explicitly models modality-specific relations through content-adaptive attention and cross-frame audio-visual connections, while Mamba captures long-range temporal cont...
| Authors | Guo-Ruei Tseng, Hung-Shin Lee, Hsin-Min Wang et al. |
|---|---|
| Primary category | eess.AS |
| Published | 16 September 2026 |
| Last updated at source | 18 September 2026 |
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