  Audio super-resolution is the task of constructing a high-resolution (HR)
audio from a low-resolution (LR) audio by adding the missing band. Previous
methods based on convolutional neural networks and mean squared error training
objective have relatively low performance, while adversarial generative models
are difficult to train and tune. Recently, normalizing flow has attracted a lot
of attention for its high performance, simple training and fast inference. In
this paper, we propose WSRGlow, a Glow-based waveform generative model to
perform audio super-resolution. Specifically, 1) we integrate WaveNet and Glow
to directly maximize the exact likelihood of the target HR audio conditioned on
LR information; and 2) to exploit the audio information from low-resolution
audio, we propose an LR audio encoder and an STFT encoder, which encode the LR
information from the time domain and frequency domain respectively. The
experimental results show that the proposed model is easier to train and
outperforms the previous works in terms of both objective and perceptual
quality. WSRGlow is also the first model to produce 48kHz waveforms from 12kHz
LR audio.
