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For the difficulty and large computational complexity of modeling morefrequency bands, full-band speech enhancement based on deep neural networks isstill challenging. Previous studies usually adopt compressed full-band speechfeatures in Bark and ERB scale with relatively low frequency resolution,leading to degraded performance, especially in the high-frequency region. Inthis paper, we propose a decoupling-style multi-band fusion model to performfull-band speech denoising and dereverberation. Instead of optimizing thefull-band speech by a single network structure, we decompose the full-bandtarget into multi sub-band speech features and then employ a multi-stage chainoptimization strategy to estimate clean spectrum stage by stage. Specifically,the low- (0-8 kHz), middle- (8-16 kHz), and high-frequency (16-24 kHz) regionsare mapped by three separate sub-networks and are then fused to obtain thefull-band clean target STFT spectrum. Comprehensive experiments on two publicdatasets demonstrate that the proposed method outperforms previous advancedsystems and yields promising performance in terms of speech quality andintelligibility in real complex scenarios.

DMF-Net: A decoupling-style multi-band fusion model for full-band speech enhancement