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RaD-Net: A Repairing and Denoising Network for Speech Signal Improvement

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arxiv 2401.04389 v1 pith:KA5WL46S submitted 2024-01-09 cs.SD eess.AS

Mingshuai Liu , Zhuangqi Chen , Xiaopeng Yan , Yuanjun Lv , Xianjun Xia , Chuanzeng Huang , Yijian Xiao , Lei Xie This is my paper · ORCID
classification cs.SDeess.AS
keywords networkrepairingdenoisingdiscriminatorsimprovementmodelrad-netsignal
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper introduces our repairing and denoising network (RaD-Net) for the ICASSP 2024 Speech Signal Improvement (SSI) Challenge. We extend our previous framework based on a two-stage network and propose an upgraded model. Specifically, we replace the repairing network with COM-Net from TEA-PSE. In addition, multi-resolution discriminators and multi-band discriminators are adopted in the training stage. Finally, we use a three-step training strategy to optimize our model. We submit two models with different sets of parameters to meet the RTF requirement of the two tracks. According to the official results, the proposed systems rank 2nd in track 1 and 3rd in track 2.

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  1. SpeechRefiner: Towards Perceptual Quality Refinement for Front-End Algorithms

    eess.AS 2025-06 conditional novelty 6.0 of 10

    SpeechRefiner, a conformer-based conditional flow matching model, improves SIGMOS perceptual quality scores on speech processed by various front-ends, including unseen systems.

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