Evaluating Musical Predictions with Multiple Versions of a Work
Bibliographic record
Abstract
The widespread use of music content analysis tools illustrates the need for diverse evaluation techniques to ensure their accuracy, robustness, reliability, and quality. This is particularly challenging in the case of features which predict musical properties whose values cannot be independently verified. Here we propose a new method for evaluating such tools that does not rely on a-priori knowledge of correct outcomes (i.e., “ground truth”). Instead, it examines many versions of a single composition, comparing predictions of musical properties expected to be relatively stable across recordings (mode, number of note events) to those expected to vary (tempo, timbre). This allows for assessing the efficacy of feature extraction even in situations where correct answers are unknown (or unknowable). As a proof of concept, we applied this approach to 17 commercially available recordings of J. S. Bach's 24 preludes from the Well-Tempered Clavier (Book 1) using three popular music content analysis tools, comparing variation in feature extraction across 17 versions of all 24 preludes (408 data points for each feature extracted). We find significant differences in the variation of mode predictions between tools, as well as more variation for predictions of mode than predictions of the number of note events. This affords a useful way of comparing predictions (whether between features or tools) which is particularly useful in the absence of ground truth. Other potential applications include parameter optimization, algorithm selection, and benchmarking procedures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".