Perceiving Music Quality with GANs

Assessing perceptual quality of musical audio signals usually requires a clean reference signal of unaltered content, hindering applications where a reference is unavailable such as for music generation. We propose training a generative adversarial network on a music library, and using its discriminator as a measure of the perceived quality of music. This method is unsupervised, needs no access to degraded material and can be tuned for various domains of music. Finally, the method is shown to have a statistically significant correlation with human ratings of music.