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.

本文提出了一种扩展的单通道实时语音增强框架 FullSubNet+，采用了轻量级多尺度时间敏感通道注意力 MulCA 模块、全幅值、实部与虚部谱图作为输入以充分利用噪声语音中的相位信息、以及用时序卷积网络块（TCN）替换原先的长短期记忆层的全幅模型，其在 DNS 挑战数据集中表现优越，达到了最先进的性能并优于其他现有语音增强方法。