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Noise suppression and echo cancellation are critical in speech enhancementand essential for smart devices and real-time communication. Deployed in voiceprocessing front-ends and edge devices, these algorithms must ensure efficientreal-time inference with low computational demands. Traditional edge-basednoise suppression often uses MSE-based amplitude spectrum mask training, butthis approach has limitations. We introduce a novel projection loss function,diverging from MSE, to enhance noise suppression. This method uses projectiontechniques to isolate key audio components from noise, significantly improvingmodel performance. For echo cancellation, the function enables directpredictions on LAEC pre-processed outputs, substantially enhancing performance.Our noise suppression model achieves near state-of-the-art results with only3.1M parameters and 0.4GFlops/s computational load. Moreover, our echocancellation model outperforms replicated industry-leading models, introducinga new perspective in speech enhancement.

CheapNET: Improving Light-weight speech enhancement network by projected loss function