Bibliographic record
Abstract
Music software applications often require similarity-finding measures. In this study, we describe an empirically derived measure for determining similarity between two melodies with multiple-note changes. The derivation of our final model involved three stages. In Stage 1, eight standard melodies were systematically varied with respect to pitch distance, pitch direction, tonal stability, metric salience and melodic contour. Comparison melodies with a one-note change were presented in transposed and nontransposed conditions. For the nontransposed condition, predictors of explained variance in similarity ratings were pitch distance, pitch direction and melodic contour. For the transposed condition, predictors were tonal stability and melodic contour. In Stage 2, we added the effects of primacy and recency. In Stage 3, comparison melodies with two-note changes were introduced, which allowed us to derive a more generalizable model capable of accommodating multiple-note changes. In a follow-up experiment, we show that our empirically derived measure of melodic similarity yielded superior performance to the Mongeau and Sankoff similarity measure. An empirically derived measure, such as the one described here, has the potential to extend the domain of similarity-finding methods in music information retrieval, on the basis of psychological predictors.
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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 | 0.004 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".