# NU-GAN: High resolution neural upsampling with GAN

arXiv: https://arxiv.org/abs/2010.11362 · HTML: https://mlsanity.com/paper/2010.11362/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 11 of 11)

- [Neural Vocoder is All You Need for Speech Super-resolution](https://mlsanity.com/paper/2203.14941/) (2022)
- [AERO: Audio Super Resolution in the Spectral Domain](https://mlsanity.com/paper/2211.12232/) (2022)
- [Self-Attention for Audio Super-Resolution](https://mlsanity.com/paper/2108.11637/) (2021)
- [Wave-U-Mamba: An End-To-End Framework For High-Quality And Efficient Speech Super Resolution](https://mlsanity.com/paper/2409.09337/) (2024)
- [Self-Supervised Speech Quality Estimation and Enhancement Using Only Clean Speech](https://mlsanity.com/paper/2402.16321/) (2024)
- [EDNet: A Versatile Speech Enhancement Framework with Gating Mamba Mechanism and Phase Shift-Invariant Training](https://mlsanity.com/paper/2506.16231/) (2025)
- [Nonparallel High-Quality Audio Super Resolution with Domain Adaptation and Resampling CycleGANs](https://mlsanity.com/paper/2210.15887/) (2022)
- [A High-Fidelity Speech Super Resolution Network using a Complex Global Attention Module with Spectro-Temporal Loss](https://mlsanity.com/paper/2507.00229/) (2025)
- [Speech Bandwidth Expansion Via High Fidelity Generative Adversarial Networks](https://mlsanity.com/paper/2407.18571/) (2024)
- [SUBARU: A Practical Approach to Power Saving in Hearables Using SUB-Nyquist Audio Resolution Upsampling](https://mlsanity.com/paper/2506.22321/) (2025)
- [Test-time adaptation for speech enhancement with an autoregressive speech prior](https://mlsanity.com/paper/2609.03622/) (2026)

## 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.