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.