562 citations · 795 across the 36 of their papers we have counts for
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eess.AS2024★ 2 cited
Universal Score-based Speech Enhancement with High Content Preservation
Robin Scheibler, Yusuke Fujita, Yuma Shirahata +1
We propose UNIVERSE++, a universal speech enhancement method based on score-based diffusion and adversarial training. Specifically, we improve the existing UNIVERSE model that deco…
eess.AS2024★ 30 cited
URGENT Challenge: Universality, Robustness, and Generalizability For Speech Enhancement
Wangyou Zhang, Robin Scheibler, Kohei Saijo +9
The last decade has witnessed significant advancements in deep learning-based speech enhancement (SE). However, most existing SE research has limitations on the coverage of SE sub-…