Conventionally, audio super-resolution models fixed the initial and the
target sampling rates, which necessitate the model to be trained for each pair
of sampling rates. We introduce NU-Wave 2, a diffusion model for neural audio
upsampling that enables the generation of 48 kHz audio signals from inputs of
various sampling rates with a single model. Based on the architecture of
NU-Wave, NU-Wave 2 uses short-time Fourier convolution (STFC) to generate
harmonics to resolve the main failure modes of NU-Wave, and incorporates
bandwidth spectral feature transform (BSFT) to condition the bandwidths of
inputs in the frequency domain. We experimentally demonstrate that NU-Wave 2
produces high-resolution audio regardless of the sampling rate of input while
requiring fewer parameters than other models. The official code and the audio
samples are available at this https URL

NU-Wave 2 模型是一种扩散模型，可以从不同采样率的输入产生 48KHz 音频信号，这个模型使用了短时傅里叶卷积和带宽谱特征变换方式来生成谐波，可以在不需要为每一对采样率都进行训练的情况下，生成高分辨率音频信号。

NU-Wave 2: 一个通用的神经音频上采样模型，适用于各种采样率

NU-Wave 2: A General Neural Audio Upsampling Model for Various Sampling  Rates

In this work, we introduce NU-Wave, the first neural audio upsampling model
to produce waveforms of sampling rate 48kHz from coarse 16kHz or 24kHz inputs,
while prior works could generate only up to 16kHz. NU-Wave is the first
diffusion probabilistic model for audio super-resolution which is engineered
based on neural vocoders. NU-Wave generates high-quality audio that achieves
high performance in terms of signal-to-noise ratio (SNR), log-spectral distance
(LSD), and accuracy of the ABX test. In all cases, NU-Wave outperforms the
baseline models despite the substantially smaller model capacity (3.0M
parameters) than baselines (5.4-21%). The audio samples of our model are
available at this https URL, and the code will be made
available soon.

NU-Wave 是第一个基于神经声码器设计的扩散概率模型进行音频超分辨率处理，可以将粗糙的 16kHz 或 24kHz 的采样率升高到 48kHz。NU-Wave 在各项性能测试中表现优异，且仅需要 3.0M 的参数，比基线模型小很多。