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.

本研究针对音频超分辨率中存在的具有挑战性的病态问题，提出了FLowHigh方法，通过将高效的生成模型流匹配技术应用于音频超分辨率中，创新性地提升了重建质量。实验结果表明，FLowHigh在VCTK基准数据集上的表现达到最先进水平，同时仅需单步采样，显著降低了计算延迟。

FLowHigh：朝着高效高质量音频超分辨率的单步流匹配方法