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.

噪声抑制和回声消除对于语音增强至关重要，是智能设备和实时通信的基础，而这些算法必须保证高效实时推断和低计算需求。我们提出了一种新的投影损失函数，用于增强噪声抑制效果，并且能够直接在 LAEC 预处理输出上进行回声消除的预测，从而显著提高模型性能。我们的噪声抑制模型仅使用 3.1M 个参数和 0.4GFlops/s 计算负载，实现了接近最先进的结果。此外，我们的回声消除模型胜过了复制的行业领先模型，引入了语音增强的新视角。