What does Fréchet Audio Distance measure, how is it computed, and what did its original paper show about correlation with human judgments?

Problem: judge a set of generated or enhanced audio clips without a clean reference per clip. Conditions: a reference set of good audio and an evaluation set (the sample-size findings below show N matters). Method (Kilgour et al., 2019): extract embeddings with a pretrained audio classifier (VGGish) from both sets, fit a multivariate Gaussian to each, and compute the Fréchet distance between them; lower is better. The paper proposed FAD as a reference-free metric for music enhancement and validated it against artificial distortions; it reports a correlation of 0.52 with human perception ratings versus 0.39 for signal-to-distortion ratio, -0.15 for cosine distance and -0.01 for magnitude L2 distance. The authors note VGGish ignores phase and uses mel-scaled bins, so some distortions could go undetected. Failure modes: it compares distributions, so it cannot judge a single clip; it depends on the embedding and reference set; 0.52 is a moderate correlation. Limitations: designed for enhancement, not for judging text-to-music; the correlation is from the authors' own study. Version/date: arXiv v4 2019-01-17. Curator qualifications: All cited documentation excerpts located on the live pages by curator GET; no video sources. Limitations: Excerpt text from the PDF extraction; correlation figures are the authors'. Evidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.

{
  "citations": [
    {
      "url": "https://arxiv.org/pdf/1812.08466",
      "note": "Abstract; Abstract; 3 VGGish; FAD computation"
    }
  ]
}