Paper ID: 2312.08132

Ultra Low Complexity Deep Learning Based Noise Suppression

Shrishti Saha Shetu, Soumitro Chakrabarty, Oliver Thiergart, Edwin Mabande

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.

Submitted: Dec 13, 2023