  Audio super-resolution is a challenging task of recovering the missing
high-resolution features from a low-resolution signal. To address this,
generative adversarial networks (GAN) have been used to achieve promising
results by training the mappings between magnitudes of the low and
high-frequency components. However, phase information is not well-considered
for waveform reconstruction in conventional methods. In this paper, we tackle
the problem of music super-resolution and conduct a thorough investigation on
the importance of phase for this task. We use GAN to predict the magnitudes of
the high-frequency components. The corresponding phase information can be
extracted using either a GAN-based waveform synthesis system or a modified
Griffin-Lim algorithm. Experimental results show that phase information plays
an important role in the improvement of the reconstructed music quality.
Moreover, our proposed method significantly outperforms other state-of-the-art
methods in terms of objective evaluations.
