# FLowHigh: Towards Efficient and High-Quality Audio Super-Resolution with Single-Step Flow Matching

arXiv: https://arxiv.org/abs/2501.04926 · HTML: https://mlsanity.com/paper/2501.04926/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Placed in Audio, speech and music, one of 14 directions in Diffusion models.
- Builds on 32 of its 46 references that are in the map.
- First posted on arXiv 9 Jan 2025.

## Placed in

- Diffusion models / [Audio, speech and music](https://mlsanity.com/diffusion/audio-speech-and-music/)

## Builds on (most cited first, 30 of 32)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow](https://mlsanity.com/paper/2209.03003/) (2022)
- [Improving and generalizing flow-based generative models with minibatch optimal transport](https://mlsanity.com/paper/2302.00482/) (2023)
- [AudioLDM: Text-to-Audio Generation with Latent Diffusion Models](https://mlsanity.com/paper/2301.12503/) (2023)
- [Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech](https://mlsanity.com/paper/2105.06337/) (2021)
- [Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale](https://mlsanity.com/paper/2306.15687/) (2023)
- [AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining](https://mlsanity.com/paper/2308.05734/) (2023)
- [BigVGAN: A Universal Neural Vocoder with Large-Scale Training](https://mlsanity.com/paper/2206.04658/) (2022)
- [Matcha-TTS: A fast TTS architecture with conditional flow matching](https://mlsanity.com/paper/2309.03199/) (2023)
- [Audiobox: Unified Audio Generation with Natural Language Prompts](https://mlsanity.com/paper/2312.15821/) (2023)
- [ViSQOL v3: An Open Source Production Ready Objective Speech and Audio Metric](https://mlsanity.com/paper/2004.09584/) (2020)
- [Generative Pre-training for Speech with Flow Matching](https://mlsanity.com/paper/2310.16338/) (2023)
- [NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling](https://mlsanity.com/paper/2104.02321/) (2021)
- [AudioSR: Versatile Audio Super-resolution at Scale](https://mlsanity.com/paper/2309.07314/) (2023)
- [Neural Vocoder is All You Need for Speech Super-resolution](https://mlsanity.com/paper/2203.14941/) (2022)
- [Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations](https://mlsanity.com/paper/1909.06628/) (2019)
- [NU-Wave 2: A General Neural Audio Upsampling Model for Various Sampling Rates](https://mlsanity.com/paper/2206.08545/) (2022)
- [Conditioning and Sampling in Variational Diffusion Models for Speech Super-Resolution](https://mlsanity.com/paper/2210.15793/) (2022)
- [WSRGlow: A Glow-based Waveform Generative Model for Audio Super-Resolution](https://mlsanity.com/paper/2106.08507/) (2021)
- [EmoSphere++: Emotion-Controllable Zero-Shot Text-to-Speech via Emotion-Adaptive Spherical Vector](https://mlsanity.com/paper/2411.02625/) (2024)
- [AERO: Audio Super Resolution in the Spectral Domain](https://mlsanity.com/paper/2211.12232/) (2022)
- [DiffProsody: Diffusion-based Latent Prosody Generation for Expressive Speech Synthesis with Prosody Conditional Adversarial Training](https://mlsanity.com/paper/2307.16549/) (2023)
- [MusicFlow: Cascaded Flow Matching for Text Guided Music Generation](https://mlsanity.com/paper/2410.20478/) (2024)
- [PeriodWave: Multi-Period Flow Matching for High-Fidelity Waveform Generation](https://mlsanity.com/paper/2408.07547/) (2024)

## Built on by (most cited first, 18 of 18)

- [Audio Super-Resolution with Latent Bridge Models](https://mlsanity.com/paper/2509.17609/) (2025)
- [A2SB: Audio-to-Audio Schrodinger Bridges](https://mlsanity.com/paper/2501.11311/) (2025)
- [FlowLUT: Efficient Image Enhancement via Differentiable LUTs and Iterative Flow Matching](https://mlsanity.com/paper/2509.23608/) (2025)
- [UniverSR: Unified and Versatile Audio Super-Resolution via Vocoder-Free Flow Matching](https://mlsanity.com/paper/2510.00771/) (2025)
- [PolypFlow: Reinforcing Polyp Segmentation with Flow-Driven Dynamics](https://mlsanity.com/paper/2502.19037/) (2025)
- [UniFlowRestore: A General Video Restoration Framework via Flow Matching and Prompt Guidance](https://mlsanity.com/paper/2504.09069/) (2025)
- [ImmersiveFlow: Stereo-to-7.1.4 spatial audio generation with flow matching](https://mlsanity.com/paper/2601.12950/) (2026)
- [CodecFlow: Efficient Bandwidth Extension via Conditional Flow Matching in Neural Codec Latent Space](https://mlsanity.com/paper/2603.02022/) (2026)
- [LatentFlowSR: High-Fidelity Audio Super-Resolution via Noise-Robust Latent Flow Matching](https://mlsanity.com/paper/2604.09188/) (2026)
- [Discriminating real and synthetic super-resolved audio samples using embedding-based classifiers](https://mlsanity.com/paper/2601.03443/) (2026)
- [Single-step Controllable Music Bandwidth Extension With Flow Matching](https://mlsanity.com/paper/2601.14356/) (2026)
- [FastWave: Optimized Diffusion Model for Audio Super-Resolution](https://mlsanity.com/paper/2603.04122/) (2026)
- [ImmersiveTTS: Environment-Aware Text-to-Speech with Multimodal Diffusion Transformer and Domain-Specific Representation Alignment](https://mlsanity.com/paper/2605.30965/) (2026)
- [VeRe-Flow: Guiding Flow Matching toward Clean Speech via Velocity Contrastive Regularization and Representation Alignment for Noise-Robust Bandwidth Expansion](https://mlsanity.com/paper/2606.29450/) (2026)
- [On the Geometry of Music Bandwidth Extension in Latent Spaces of Audio Codecs](https://mlsanity.com/paper/2608.03721/) (2026)
- [GeoFlow: Efficient Driving Video Generation via Geometry-Aligned Priors](https://mlsanity.com/paper/2608.12203/) (2026)
- [P2Flow: Phoneme-aware Progressive Flow Matching for Extreme Speech Super-Resolution](https://mlsanity.com/paper/2609.24138/) (2026)
- [End-to-End Historical Music Restoration in Latent Space](https://mlsanity.com/paper/2610.00607/) (2026)

## Abstract (arXiv)

Audio super-resolution is challenging owing to its ill-posed nature. Recently, the application of diffusion models in audio super-resolution has shown promising results in alleviating this challenge. However, diffusion-based models have limitations, primarily the necessity for numerous sampling steps, which causes significantly increased latency when synthesizing high-quality audio samples. In this paper, we propose FLowHigh, a novel approach that integrates flow matching, a highly efficient generative model, into audio super-resolution. We also explore probability paths specially tailored for audio super-resolution, which effectively capture high-resolution audio distributions, thereby enhancing reconstruction quality. The proposed method generates high-fidelity, high-resolution audio through a single-step sampling process across various input sampling rates. The experimental results on the VCTK benchmark dataset demonstrate that FLowHigh achieves state-of-the-art performance in audio super-resolution, as evaluated by log-spectral distance and ViSQOL while maintaining computational efficiency with only a single-step sampling process.