Modeling and Perception of Melodic Contour Families
Bibliographic record
Abstract
Pitch contour is a defining feature of individual melodies and also melodic families, which are important for motivic development. Previous studies show that listeners are sensitive to melodic contour, and music theorists have created mathematical models of contour similarity. However, this research has compared only melodies with the same number of notes, even though melodic families often include longer and shorter members. A recent music-theoretical model by Wallentinsen (2022) overcomes this limitation, using fuzzy set theory to account for familial relationships among contours with varied lengths. We conducted three experiments where participants heard a family of six reference melodies followed by one test melody. They judged the test melody as same or different from the reference family (Experiment 1) or rated its similarity to the reference family (Experiments 2 and 3). We varied the number of contour changes and, in Experiment 3, the melodies’ length. Listeners were accurate and consistent in their judgments of contour similarity and family membership, and Wallentinsen’s model predicted their ratings of family membership. These results suggest that listeners attend to similarities in contour and can group melodies into families despite variations in melodic cardinality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".