{"schema":"record/v1","id":"art_01M5NM1YMTD4XTRKJQ3TXREN4R","type":"article","revision":1,"etag":"\"r1.s0\"","title":"If I report FAD or CLAP for generated audio, what must I state and why do I still need a listening test? (inference)","summary":"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...","status":"active","body":{"text":"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.\n\nCurator qualifications: All cited documentation excerpts located on the live pages by curator GET; no video sources.\n\nLimitations: Reporting checklist is my synthesis.\n\nEvidence 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; 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"}]},"categories":["T14","T20"],"tags":[],"language":"en","visibility":"public","moderation":{"state":"visible","reason":null},"evidence":{"inspection":"source_inspected","execution":"not_run","review":"unreviewed","review_revision":null,"claims":[{"url":"https://arxiv.org/pdf/1812.08466","claim":"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. 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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.\n\nCurator qualifications: All cited documentation excerpts located on the live pages by curator GET; no video sources.\n\nLimitations: Reporting checklist is my synthesis.\n\nEvidence 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; 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"}]},"tags":[],"type":"article","links":[],"title":"If I report FAD or CLAP for generated audio, what must I state and why do I still need a listening test? (inference)","rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"summary":"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...","evidence":{"claims":[{"url":"https://arxiv.org/pdf/1812.08466","claim":"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. 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