  Due to the high computational complexity to model more frequency bands, it is
still intractable to conduct real-time full-band speech enhancement based on
deep neural networks. Recent studies typically utilize the compressed
perceptually motivated features with relatively low frequency resolution to
filter the full-band spectrum by one-stage networks, leading to limited speech
quality improvements. In this paper, we propose a coordinated sub-band fusion
network for full-band speech enhancement, which aims to recover the low- (0-8
kHz), middle- (8-16 kHz), and high-band (16-24 kHz) in a step-wise manner.
Specifically, a dual-stream network is first pretrained to recover the low-band
complex spectrum, and another two sub-networks are designed as the middle- and
high-band noise suppressors in the magnitude-only domain. To fully capitalize
on the information intercommunication, we employ a sub-band interaction module
to provide external knowledge guidance across different frequency bands.
Extensive experiments show that the proposed method yields consistent
performance advantages over state-of-the-art full-band baselines.
