GAP-URGENet: A Generative-Predictive Fusion Framework for Universal Speech Enhancement
We introduce GAP-URGENet, a generative-predictive fusion framework developed for Track 1 of the ICASSP 2026 URGENT Challenge. The system integrates a generative branch, which performs full-stack speech restoration in a self-supervised representation domain and reconstructs the waveform via a neural vocoder, along with a predictive branch that performs spectrogram-domain enhancement, providing complementary cues. Outputs from both branches are fused by a post-processing module, which also performs bandwidth extension to generate the enhanced waveform at 48 kHz, later downsampled to the original sampling rate. This generative-predictive fusion improves robustness and perceptual quality, ach...
| Authors | Xiaobin Rong, Yushi Wang, Zheng Wang et al. |
|---|---|
| Primary category | eess.AS |
| Published | 2 April 2026 |
| Last updated at source | 2 April 2026 |
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