Computer Science > Sound
[Submitted on 23 Mar 2022 (v1), last revised 26 Mar 2022 (this version, v2)]
Title:FullSubNet+: Channel Attention FullSubNet with Complex Spectrograms for Speech Enhancement
View PDFAbstract: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.
Submission history
From: Jun Chen [view email][v1] Wed, 23 Mar 2022 04:33:09 UTC (315 KB)
[v2] Sat, 26 Mar 2022 19:20:53 UTC (313 KB)
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