  Upsampling artifacts are caused by problematic upsampling layers and due to
spectral replicas that emerge while upsampling. Also, depending on the used
upsampling layer, such artifacts can either be tonal artifacts (additive
high-frequency noise) or filtering artifacts (substractive, attenuating some
bands). In this work we investigate the practical implications of having
upsampling artifacts in the resulting audio, by studying how different
artifacts interact and assessing their impact on the models' performance. To
that end, we benchmark a large set of upsampling layers for music source
separation: different transposed and subpixel convolution setups, different
interpolation upsamplers (including two novel layers based on stretch and sinc
interpolation), and different wavelet-based upsamplers (including a novel
learnable wavelet layer). Our results show that filtering artifacts, associated
with interpolation upsamplers, are perceptually preferrable, even if they tend
to achieve worse objective scores.
