Empirical comparisons of pitch patterns in music, speech, and birdsong
Bibliographic record
Abstract
In music, large intervals ("pitch skips") are often followed by reversals, and phrases often have an arch-like shape and final durational lengthening. These regularities could reflect motor constraints on pitch production or could reflect the melodic characteristics of speech. To distinguish between these possibilities we compared pitch patterns in instrumental musical themes, sentences, and birdsongs. Patterns due to production-related constraints should be common to all three domains, whereas patterns due to statistical learning from speech should be present in speech but not birdsong. Sequences were taken from English and French instrumental classical music, sentences from 4 languages, and songs of 56 songbird families. For sentences and birdsongs each syllable/note was assigned one pitch. For each sequence, we quantified patterns of post-skip reversals, the direction of the initial and final interval, the relative duration of the final syllable/note, and the pitch contour shape. Post-skip reversals predominated in all domains, likely reflecting a shared constraint: skips frequently take melodies toward the edges of the pitch range, forcing a subsequent reversal (as suggested by Von Hippel & Huron, 2000). Arch-like contours and final lengthening were found in music and speech but not birdsong, possibly reflecting an influence of speech patterns on musical structure.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".