If I report FAD or CLAP for generated audio, what must I state and why do I still need a listening test? (inference)
Problem: automatic scores look objective but hide settings. Conditions: any report of FAD, KAD or CLAP score. Method (inference from the round's papers): report the embedding and checkpoint, the reference set, N (number of clips and frames), whether bias was corrected, and the prompt set; do not compare FAD across papers with different embeddings or N; use per-song FAD to find outliers rather than as a verdict; use CLAP score only as a text-adherence proxy and name the checkpoint. Add a listening test with hidden reference and anchors (see C3-B570-15 for MUSHRA) because: original FAD correlated only moderately with human ratings (0.52); VGGish correlated poorly with music quality ratings in the Gui study (n=10 raters); and the human-preference study found model-level metrics can misrank individual models. Also keep automatic MOS predictors in context (C3-B570-14). Failure modes: optimising to a metric (Goodhart); reporting a single number. Limitations: no source gives a minimum N or...
Structured fields
{
"citations": [
{
"url": "https://arxiv.org/pdf/1812.08466",
"note": "Abstract; 4.4 Results; 3 Sample size bias; V. Results"
},
{
"url": "https://arxiv.org/pdf/2311.01616",
"note": "Abstract; 4.4 Results; 3 Sample size bias; V. Results"
},
{
"url": "https://arxiv.org/pdf/2502.15602",
"note": "Abstract; 4.4 Results; 3 Sample size bias; V. Results"
},
{
"url": "https://arxiv.org/pdf/2506.19085",
"note": "Abstract; 4.4 Results; 3 Sample size bias; V. Results"
}
]
}Evidence claims
- [sourced_fact] Problem: automatic scores look objective but hide settings. Conditions: any report of FAD, KAD or CLAP score. Method (inference from the round's papers): report the embedding and checkpoint, the reference set, N (number of clips and frames), whether bias was corrected, and the prompt set; do not compare FAD across papers with different embeddings or N; use per-song FAD to find outliers rather than as a verdict; use CLAP score only as a text-adherence proxy and name the checkpoint. Add a listening test with hidden reference and anchors (see C3-B570-15 for MUSHRA) because: original FAD correlated only moderately with human ratings (0.52); VGGish correlated poorly with music quality ratings in the Gui study (n=10 raters); and the human-preference study found model-level metrics can misrank individual models. Also keep automatic MOS predictors in context (C3-B570-14). Failure modes: optimising to a metric (Goodhart); reporting a single number. Limitations: no source gives a minimum N or rater count that is valid for all uses; the studies report their own sizes only. Version/date: synthesis 2026-10-23. · locator: Abstract; 4.4 Results; 3 Sample size bias; V. Results source
Rights
| component | license | hosting | redistribution | modification | training | Evidence and notes |
|---|---|---|---|---|---|---|
| documentation | unknown | permitted | permitted | unknown | unknown | Applies to this original summary only, not to the cited sources. |
Linked records
- cites → https://arxiv.org/pdf/1812.08466
- cites → https://arxiv.org/pdf/2311.01616
- cites → https://arxiv.org/pdf/2502.15602
- cites → https://arxiv.org/pdf/2506.19085