This paper introduces an innovative method for reducing the computational
complexity of deep neural networks in real-time speech enhancement on
resource-constrained devices. The proposed approach utilizes a two-stage
processing framework, employing channelwise feature reorientation to reduce the
computational load of convolutional operations. By combining this with a
modified power law compression technique for enhanced perceptual quality, this
approach achieves noise suppression performance comparable to state-of-the-art
methods with significantly less computational requirements. Notably, our
algorithm exhibits 3 to 4 times less computational complexity and memory usage
than prior state-of-the-art approaches.

一种创新的方法用于在资源受限设备上实时语音增强的深度神经网络计算复杂度的减少，该方法利用两阶段处理框架，采用通道特征重定向来降低卷积运算的计算负荷，并结合改进的功率定律压缩技术以实现与最先进方法相当的噪声抑制性能，但具有显著较少的计算要求。值得注意的是，我们的算法的计算复杂度和内存使用比之前最先进的方法要少 3 到 4 倍。

基于极低复杂度深度学习的噪声抑制

Ultra Low Complexity Deep Learning Based Noise Suppression

Streaming models are an essential component of real-time speech enhancement
tools. The streaming regime constrains speech enhancement models to use only a
tiny context of future information, thus, the low-latency streaming setup is
generally assumed to be challenging and has a significant negative effect on
the model quality. However, due to the sequential nature of streaming
generation, it provides a natural possibility for autoregression, i.e., using
previous predictions when making current ones. In this paper, we present a
simple, yet effective trick for training of autoregressive low-latency speech
enhancement models. We demonstrate that the proposed technique leads to stable
improvement across different architectures and training scenarios.

本文介绍了一种简单、有效的技巧，用于训练自回归低延迟语音增强模型，证明了该技术在不同架构和训练场景下均导致了稳定的改进。