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Due to the high computational complexity to model more frequency bands, it isstill intractable to conduct real-time full-band speech enhancement based ondeep neural networks. Recent studies typically utilize the compressedperceptually motivated features with relatively low frequency resolution tofilter the full-band spectrum by one-stage networks, leading to limited speechquality improvements. In this paper, we propose a coordinated sub-band fusionnetwork for full-band speech enhancement, which aims to recover the low- (0-8kHz), 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-bandcomplex spectrum, and another two sub-networks are designed as the middle- andhigh-band noise suppressors in the magnitude-only domain. To fully capitalizeon the information intercommunication, we employ a sub-band interaction moduleto provide external knowledge guidance across different frequency bands.Extensive experiments show that the proposed method yields consistentperformance advantages over state-of-the-art full-band baselines.

Optimizing Shoulder to Shoulder: A Coordinated Sub-Band Fusion Model for Real-Time Full-Band Speech Enhancement