Two Heads Are Better Than One: A Two-Stage Approach for Monaural Noise Reduction in the Complex Domain
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Abstract (arXiv)
In low signal-to-noise ratio conditions, it is difficult to effectively recover the magnitude and phase information simultaneously. To address this problem, this paper proposes a two-stage algorithm to decouple the joint optimization problem w.r.t. magnitude and phase into two sub-tasks. In the first stage, only magnitude is optimized, which incorporates noisy phase to obtain a coarse complex clean speech spectrum estimation. In the second stage, both the magnitude and phase components are refined. The experiments are conducted on the WSJ0-SI84 corpus, and the results show that the proposed approach significantly outperforms previous baselines in terms of PESQ, ESTOI, and SDR.
Key facts. Cited by 3 papers in the map (data through 19 Oct 2026). First posted on arXiv 3 Nov 2020.
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Citing papers per year: 2021: 3.
3 papers.
- 2021Speech Enhancement Using Multi-Stage Self-Attentive Temporal Convolutional Networkscited by 9
- 2021Deep Interaction between Masking and Mapping Targets for Single-Channel Speech Enhancementcited by 1
- 2021Incorporating Multi-Target in Multi-Stage Speech Enhancement Model for Better Generalizationcited by 1
- 2021Incorporating Multi-Target in Multi-Stage Speech Enhancement Model for Better Generalization9 Jul 2021
- 2021Deep Interaction between Masking and Mapping Targets for Single-Channel Speech Enhancement9 Jun 2021
- 2021Speech Enhancement Using Multi-Stage Self-Attentive Temporal Convolutional Networks24 Feb 2021