  We propose the Fr\'echet Audio Distance (FAD), a novel, reference-free
evaluation metric for music enhancement algorithms. We demonstrate how typical
evaluation metrics for speech enhancement and blind source separation can fail
to accurately measure the perceived effect of a wide variety of distortions. As
an alternative, we propose adapting the Fr\'echet Inception Distance (FID)
metric used to evaluate generative image models to the audio domain. FAD is
validated using a wide variety of artificial distortions and is compared to the
signal based metrics signal to distortion ratio (SDR), cosine distance and
magnitude L2 distance. We show that, with a correlation coefficient of 0.52,
FAD correlates more closely with human perception than either SDR, cosine
distance or magnitude L2 distance, with correlation coefficients of 0.39, -0.15
and -0.01 respectively.
