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.

翻译：本文提出了一种创新方法，用于降低深度神经网络在资源受限设备上实时语音增强中的计算复杂度。该方法采用两阶段处理框架，通过通道特征重定向来减少卷积操作的计算负载。将这一技术与改进的幂律压缩技术相结合以增强感知质量，该方法在显著降低计算需求的同时，实现了与最先进方法相当的噪声抑制性能。值得注意的是，我们的算法相比先前最先进的方法，计算复杂度和内存使用量降低了3到4倍。