  The growing popularity of generative music models underlines the need for
perceptually relevant, objective music quality metrics. The Frechet Audio
Distance (FAD) is commonly used for this purpose even though its correlation
with perceptual quality is understudied. We show that FAD performance may be
hampered by sample size bias, poor choice of audio embeddings, or the use of
biased or low-quality reference sets. We propose reducing sample size bias by
extrapolating scores towards an infinite sample size. Through comparisons with
MusicCaps labels and a listening test we identify audio embeddings and music
reference sets that yield FAD scores well-correlated with acoustic and musical
quality. Our results suggest that per-song FAD can be useful to identify
outlier samples and predict perceptual quality for a range of music sets and
generative models. Finally, we release a toolkit that allows adapting FAD for
generative music evaluation.
