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

arXiv eess.ASby [Submitted on 2 Apr 2026]April 3, 20261 min read1 views

arXiv:2604.01832v1 Announce Type: new 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 rank

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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 this https URL.

Comments: Awarded 1st place in the URGENT 2026 Challenge (objective phase), accepted by ICASSP 2026

Subjects:

Audio and Speech Processing (eess.AS); Sound (cs.SD)

Cite as: arXiv:2604.01832 [eess.AS]

(or arXiv:2604.01832v1 [eess.AS] for this version)

https://doi.org/10.48550/arXiv.2604.01832

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Xiaobin Rong [view email] [v1] Thu, 2 Apr 2026 09:45:58 UTC (88 KB)

Original source

arXiv eess.AS

https://arxiv.org/abs/2604.01832

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