NU-GAN: High resolution neural upsampling with GAN
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Abstract (arXiv)
In this paper, we propose NU-GAN, a new method for resampling audio from lower to higher sampling rates (upsampling). Audio upsampling is an important problem since productionizing generative speech technology requires operating at high sampling rates. Such applications use audio at a resolution of 44.1 kHz or 48 kHz, whereas current speech synthesis methods are equipped to handle a maximum of 24 kHz resolution. NU-GAN takes a leap towards solving audio upsampling as a separate component in the text-to-speech (TTS) pipeline by leveraging techniques for audio generation using GANs. ABX preference tests indicate that our NU-GAN resampler is capable of resampling 22 kHz to 44.1 kHz audio that is distinguishable from original audio only 7.4% higher than random chance for single speaker dataset, and 10.8% higher than chance for multi-speaker dataset.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Oct 2020.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 0; 2024: 3; 2025: 3; 2026: 1.
11 papers.
- 2022Neural Vocoder is All You Need for Speech Super-resolutioncited by 37
- 2022AERO: Audio Super Resolution in the Spectral Domaincited by 17
- 2021Self-Attention for Audio Super-Resolutioncited by 9
- 2024Wave-U-Mamba: An End-To-End Framework For High-Quality And Efficient Speech Super Resolutioncited by 9
- 2024Self-Supervised Speech Quality Estimation and Enhancement Using Only Clean Speechcited by 7
- 2025EDNet: A Versatile Speech Enhancement Framework with Gating Mamba Mechanism and Phase Shift-Invariant Trainingcited by 3
- 2022Nonparallel High-Quality Audio Super Resolution with Domain Adaptation and Resampling CycleGANscited by 2
- 2025A High-Fidelity Speech Super Resolution Network using a Complex Global Attention Module with Spectro-Temporal Losscited by 1
- 2024Speech Bandwidth Expansion Via High Fidelity Generative Adversarial Networkscited by 0
- 2025SUBARU: A Practical Approach to Power Saving in Hearables Using SUB-Nyquist Audio Resolution Upsamplingcited by 0
- 2026Test-time adaptation for speech enhancement with an autoregressive speech priorcited by 0
- 2026Test-time adaptation for speech enhancement with an autoregressive speech prior3 Sep 2026
- 2025A High-Fidelity Speech Super Resolution Network using a Complex Global Attention Module with Spectro-Temporal Loss30 Jun 2025
- 2025SUBARU: A Practical Approach to Power Saving in Hearables Using SUB-Nyquist Audio Resolution Upsampling27 Jun 2025
- 2025EDNet: A Versatile Speech Enhancement Framework with Gating Mamba Mechanism and Phase Shift-Invariant Training19 Jun 2025
- 2024Wave-U-Mamba: An End-To-End Framework For High-Quality And Efficient Speech Super Resolution14 Sep 2024
- 2024Speech Bandwidth Expansion Via High Fidelity Generative Adversarial Networks26 Jul 2024
- 2024Self-Supervised Speech Quality Estimation and Enhancement Using Only Clean Speech26 Feb 2024
- 2022AERO: Audio Super Resolution in the Spectral Domain22 Nov 2022
- 2022Nonparallel High-Quality Audio Super Resolution with Domain Adaptation and Resampling CycleGANs28 Oct 2022
- 2022Neural Vocoder is All You Need for Speech Super-resolution28 Mar 2022
- 2021Self-Attention for Audio Super-Resolution26 Aug 2021