  Fast Fourier convolution (FFC) is the recently proposed neural operator
showing promising performance in several computer vision problems. The FFC
operator allows employing large receptive field operations within early layers
of the neural network. It was shown to be especially helpful for inpainting of
periodic structures which are common in audio processing. In this work, we
design neural network architectures which adapt FFC for speech enhancement. We
hypothesize that a large receptive field allows these networks to produce more
coherent phases than vanilla convolutional models, and validate this hypothesis
experimentally. We found that neural networks based on Fast Fourier convolution
outperform analogous convolutional models and show better or comparable results
with other speech enhancement baselines.
