  This paper proposes a full-band and sub-band fusion model, named as
FullSubNet, for single-channel real-time speech enhancement. Full-band and
sub-band refer to the models that input full-band and sub-band noisy spectral
feature, output full-band and sub-band speech target, respectively. The
sub-band model processes each frequency independently. Its input consists of
one frequency and several context frequencies. The output is the prediction of
the clean speech target for the corresponding frequency. These two types of
models have distinct characteristics. The full-band model can capture the
global spectral context and the long-distance cross-band dependencies. However,
it lacks the ability to modeling signal stationarity and attending the local
spectral pattern. The sub-band model is just the opposite. In our proposed
FullSubNet, we connect a pure full-band model and a pure sub-band model
sequentially and use practical joint training to integrate these two types of
models' advantages. We conducted experiments on the DNS challenge (INTERSPEECH
2020) dataset to evaluate the proposed method. Experimental results show that
full-band and sub-band information are complementary, and the FullSubNet can
effectively integrate them. Besides, the performance of the FullSubNet also
exceeds that of the top-ranked methods in the DNS Challenge (INTERSPEECH 2020).
