  We study the ability of Wasserstein Generative Adversarial Network (WGAN) to
generate missing audio content which is, in context, (statistically similar) to
the sound and the neighboring borders. We deal with the challenge of audio
inpainting long range gaps (500 ms) using WGAN models. We improved the quality
of the inpainting part using a new proposed WGAN architecture that uses a
short-range and a long-range neighboring borders compared to the classical WGAN
model. The performance was compared with two different audio instruments (piano
and guitar) and on virtuoso pianists together with a string orchestra. The
objective difference grading (ODG) was used to evaluate the performance of both
architectures. The proposed model outperforms the classical WGAN model and
improves the reconstruction of high-frequency content. Further, we got better
results for instruments where the frequency spectrum is mainly in the lower
range where small noises are less annoying for human ear and the inpainting
part is more perceptible. Finally, we could show that better test results for
audio dataset were reached where a particular instrument is accompanist by
other instruments if we train the network only on this particular instrument
neglecting the other instruments.
