Discriminator: Next, we removed or changed the discriminators one-by-one, to see their impact on
the final result. First, we find that the multi-band STFT discriminator does not result in significantly
better metrics, except for SI-SDR, where it is slightly better. However, when inspecting spectrograms
of generated waveforms, we find that the multi-band discriminator alleviates aliasing of high fre-
quencies. The upsampling layers of the decoder introduce significant aliasing artifacts [29]. The
multi-band discriminator is more easily able to detect these aliasing artifacts and give feedback to the
generator to remove them. Since aliasing artifacts are very small in terms of magnitude, their effect
on our objective metrics is minimal. Thus, we keep the multi-band discriminator.
We find that adversarial losses are critical to both the quality of the output audio, as well as the bitrate
efficiency. When training with only reconstruction loss, the bitrate efficiency drops from 99% to
RVQGAN