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Beyond Model Size: Redesigning LiSenNet for embedded speech enhancement
Deploying real-time speech enhancement on resource-constrained devices requires meeting strict latency, memory, and energy constraints. Microcontroller NPUs can accelerate neural inference under these constraints, but only through a restricted set of operators in static, integer-quantized graphs. Recent speech-enhancement networks have reduced parameter counts and MACs to levels nominally suitable for microcontrollers, but their operators and execution patterns often remain incompatible with restricted NPUs. We address this gap by redesigning LiSenNet, a 37k parameter sub-band dual-path model,
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- arXiv · AI, language, vision and robotics · 2026-09-24T14:24:43.000Z
First collected: 2026-09-25T06:12:46.948Z. This is not the publication date.