Flowhigh: Towards Efficient And High-quality Audio Super-resolution With Single-step Flow Matching

Abstract

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

Stats

• citations5
• S2 citations—
• github stars0
• HF likes0
• heat score5.84
• arxiv keyyun2025flowhigh

Related papers

• Universr: Unified And Versatile Audio Super-resolution Via Vocoder-free Flow Matching (2025)0.00
• Flashaudio: Rectified Flows For Fast And High-fidelity Text-to-audio Generation (2024)5.13
• Voiceflow: Efficient Text-to-speech With Rectified Flow Matching (2023)0.00
• Flowavse: Efficient Audio-visual Speech Enhancement With Conditional Flow Matching (2024)0.00
• Flowdec: A Flow-based Full-band General Audio Codec With High Perceptual Quality (2025)0.00
• Flashsr: One-step Versatile Audio Super-resolution Via Diffusion Distillation (2025)4.52
• Inspiremusic: Integrating Super Resolution And Large Language Model For High-fidelity Long-form Music Generation (2025)6.26
• Flowmac: Conditional Flow Matching For Audio Coding At Low Bit Rates (2024)0.00