  Noise suppression and echo cancellation are critical in speech enhancement
and essential for smart devices and real-time communication. Deployed in voice
processing front-ends and edge devices, these algorithms must ensure efficient
real-time inference with low computational demands. Traditional edge-based
noise suppression often uses MSE-based amplitude spectrum mask training, but
this approach has limitations. We introduce a novel projection loss function,
diverging from MSE, to enhance noise suppression. This method uses projection
techniques to isolate key audio components from noise, significantly improving
model performance. For echo cancellation, the function enables direct
predictions on LAEC pre-processed outputs, substantially enhancing performance.
Our noise suppression model achieves near state-of-the-art results with only
3.1M parameters and 0.4GFlops/s computational load. Moreover, our echo
cancellation model outperforms replicated industry-leading models, introducing
a new perspective in speech enhancement.
