  Reverb plays a critical role in music production, where it provides listeners
with spatial realization, timbre, and texture of the music. Yet, it is
challenging to reproduce the musical reverb of a reference music track even by
skilled engineers. In response, we propose an end-to-end system capable of
switching the musical reverb factor of two different mixed vocal tracks. This
method enables us to apply the reverb of the reference track to the source
track to which the effect is desired. Further, our model can perform
de-reverberation when the reference track is used as a dry vocal source. The
proposed model is trained in combination with an adversarial objective, which
makes it possible to handle high-resolution audio samples. The perceptual
evaluation confirmed that the proposed model can convert the reverb factor with
the preferred rate of 64.8%. To the best of our knowledge, this is the first
attempt to apply deep neural networks to converting music reverb of vocal
tracks.
