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arXiv · 2604.01832

GAP-URGENet: A Generative-Predictive Fusion Framework for Universal Speech Enhancement

Xiaobin Rong·Yushi Wang·Zheng Wang·Jing Lu
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Abstract

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, achieving top performance in the blind-test phase and ranking 1st in the objective evaluation. Audio examples are available at https://xiaobin-rong.github.io/gap-urgenet_demo.

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BibTeXRIS

Xiaobin Rong, Yushi Wang, Zheng Wang, Jing Lu. 2026-04-02. GAP-URGENet: A Generative-Predictive Fusion Framework for Universal Speech Enhancement. https://arxiv.org/abs/2604.01832

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