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