Efficient Audio-Visual Speech Enhancement via Reinforcement Learning-Based Neural Architecture Search
Audio-visual speech enhancement (AVSE) improves speech intelligibility in noisy environments but often relies on computationally expensive models unsuitable for resource-constrained edge devices. This paper introduces a reinforcement learning-driven neural architecture search (RL-NAS) framework to discover compact, efficient AVSE architectures. The search space preserves temporal alignment between audio and visual streams, while a novel reward function jointly optimises speech enhancement performance, measured by SI-SNR improvement, and model compactness. Using proximal policy optimisation with constraint-aware action masking, the framework evaluates only 35 candidate architectures and id...
| Authors | Khubaib Ahmed, Ahsan Adeel, Nasir Saleem et al. |
|---|---|
| Primary category | cs.SD |
| Published | 8 October 2026 |
| Last updated at source | 8 October 2026 |
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