Abstract
Audio Super-Resolution is a set of techniques aimed at high-quality estimation of the given signal as if it would be sampled with higher sample rate. Among suggested methods there are diffusion and flow models (which are considered slower), generative adversarial networks (which are considered faster), however both approaches are currently presented by high-parametric networks, requiring high computational costs both for training and inference. We propose a solution to both these problems by re-considering the recent advances in the training of diffusion models and applying them to super-resolution from any to 48 kHz sample rate. Our approach shows better results than NU-Wave 2 and is comparable to state-of-the-art models. Our model called FastWave has around 50 GFLOPs of computational complexity and 1.3 M parameters and can be trained with less resources and significantly faster than the majority of recently proposed diffusion- and flow-based solutions. The code has been made publicly available.
Abstract (translated)
音频超分辨率是一系列技术，旨在通过更高采样率对给定信号进行高质量估计。其中建议的方法包括扩散模型和流模型（被认为较慢），以及生成对抗网络（被认为较快）。然而，这两种方法当前都需要高参数的网络，这导致了在训练和推理过程中较高的计算成本。我们提出了一种解决方案，重新审视最近在训练扩散模型方面的进展，并将其应用于从任何采样率到48 kHz的超分辨率任务中。我们的方法比NU-Wave 2表现更好，并且与现有最佳模型相当。我们的模型名为FastWave，在计算复杂度方面约为50 GFLOPs，参数量为1.3M，能够以较少资源并且显著快于大多数最近提出的基于扩散和流的方法进行训练。代码已经公开发布。
URL
https://arxiv.org/abs/2603.04122
