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。