human
Baselines
We compare our method with WSRGlow [16] and NU-Wave,
which are aiming for high-quality 48 kHz audio. To train baseline models for general neural audio upsampling task, we use
the same VCTK dataset and the same method to generate lowresolution audio signals as in Section 4.1. While NU-Wave can
be trained without modification, original WSRGlow requires
some adjustment due to input length issues caused by downsampled input audio not passing through an upsampling process. As the lengths of inputs are unified to the original signals
for general neural audio upsampling task, we add a convolution
layer with kernel size 3 and stride 2 after conditional encoder of
2× upsampling WSRGlow model. While adjusted WSRGlow’s
number of parameters is 270M, the number of parameters for
NU-Wave and NU-Wave 2 are significantly small, 3.0M and
1.7M, respectively. Compared to other baselines, NU-Wave 2
has the fewest parameters, even fewer than NU-Wave. There
is a possibility that hyperparameters of baseline models are not
fully optimized for our experimental setting.

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