  Audio Super-Resolution (SR) is an important topic as low-resolution
recordings are ubiquitous in daily life. In this paper, we focus on the music
SR task, which is challenging due to the wide frequency response and dynamic
range of music. Many models are designed in time domain to jointly process
magnitude and phase of audio signals. However, prior works show that approaches
using Time-Domain Convolutional Neural Network (TD-CNN) tend to produce
annoying artifacts in their waveform outputs, and the cause of the artifacts is
yet to be identified. To the best of our knowledge, this work is the first to
demonstrate the artifacts in TD-CNNs are caused by the phase distortion via a
subjective experiment. We further propose Time-Domain Phase Repair (TD-PR),
which uses a neural vocoder pre-trained on the wide-band data to repair the
phase components in the waveform outputs of TD-CNNs. Although the vocoder and
TD-CNNs are independently trained, the proposed TD-PR obtained better mean
opinion score, significantly improving the perceptual quality of TD-CNN
baselines. Since the proposed TD-PR only repairs the phase components of the
waveforms, the improved perceptual quality in turn indicates that phase
distortion has been the cause of the annoying artifacts of TD-CNNs. Moreover, a
single pretrained vocoder can be directly applied to arbitrary TD-CNNs without
additional adaptation. Therefore, we apply TD-PR to three TD-CNNs that have
different architecture and parameter amount. Consistent improvements are
observed when TD-PR is applied to all three TD-CNN baselines. Audio samples are
available on the demo page.
