  Generally, Deep Neural Networks (DNNs) are expected to have high performance
when their model size is large. However, large models failed to produce
high-quality results commensurate with their scale in music Super-Resolution
(SR). We attribute this to that DNNs cannot learn information commensurate with
their size from standard mean square error losses. To unleash the potential of
large DNN models in music SR, we propose BigWavGAN, which incorporates Demucs,
a large-scale wave-to-wave model, with State-Of-The-Art (SOTA) discriminators
and adversarial training strategies. Our discriminator consists of Multi-Scale
Discriminator (MSD) and Multi-Resolution Discriminator (MRD). During inference,
since only the generator is utilized, there are no additional parameters or
computational resources required compared to the baseline model Demucs.
Objective evaluation affirms the effectiveness of BigWavGAN in music SR.
Subjective evaluations indicate that BigWavGAN can generate music with
significantly high perceptual quality over the baseline model. Notably,
BigWavGAN surpasses the SOTA music SR model in both simulated and real-world
scenarios. Moreover, BigWavGAN represents its superior generalization ability
to address out-of-distribution data. The conducted ablation study reveals the
importance of our discriminators and training strategies. Samples are available
on the demo page.
