  Previously proposed FullSubNet has achieved outstanding performance in Deep
Noise Suppression (DNS) Challenge and attracted much attention. However, it
still encounters issues such as input-output mismatch and coarse processing for
frequency bands. In this paper, we propose an extended single-channel real-time
speech enhancement framework called FullSubNet+ with following significant
improvements. First, we design a lightweight multi-scale time sensitive channel
attention (MulCA) module which adopts multi-scale convolution and channel
attention mechanism to help the network focus on more discriminative frequency
bands for noise reduction. Then, to make full use of the phase information in
noisy speech, our model takes all the magnitude, real and imaginary
spectrograms as inputs. Moreover, by replacing the long short-term memory
(LSTM) layers in original full-band model with stacked temporal convolutional
network (TCN) blocks, we design a more efficient full-band module called
full-band extractor. The experimental results in DNS Challenge dataset show the
superior performance of our FullSubNet+, which reaches the state-of-the-art
(SOTA) performance and outperforms other existing speech enhancement
approaches.
