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Several methods have been developed to assess the perceptual quality of audiounder transforms like lossy compression. However, they require paired referencesignals of the unaltered content, limiting their use in applications wherereferences are unavailable. This has hindered progress in audio generation andstyle transfer, where a no-reference quality assessment method would allow morereproducible comparisons across methods. We propose training a GAN on a largemusic library, and using its discriminator as a no-reference quality assessmentmeasure of the perceived quality of music. This method is unsupervised, needsno access to degraded material and can be tuned for various domains of music.In a listening test with 448 human subjects, where participants ratedprofessionally produced music tracks degraded with different levels and typesof signal degradations such as waveshaping distortion and low-pass filtering,we establish a dataset of human rated material. By using the human rateddataset we show that the discriminator score correlates significantly with thesubjective ratings, suggesting that the proposed method can be used to create ano-reference musical audio quality assessment measure.

Perceiving Music Quality with GANs