Music is scaled, while speech is not: A cross-cultural analysis
Bibliographic record
Abstract
Music is well-known to be based on sets of discrete pitches that are combined to form musical melodies. In contrast, there is no evidence that speech is organized into stable tonal structures analogous to musical scales. In the current study, we developed a new computational method for measuring what we call the "scaledness" of an acoustic sample and applied it to three cross-cultural ethnographic corpora of speech, song, and/or instrumental music (n = 1696 samples). The results confirmed the established notion that music is significantly more scaled than speech, but they also revealed some novel findings. First, highly prosodic speech-such as a mother talking to a baby-was no more scaled than regular speech, which contradicts intuitive notions that prosodic speech is more "tonal" than regular speech. Second, instrumental music was far more scaled than vocal music, in keeping with the observation that the voice is highly imprecise at pitch production. Finally, singing style had a significant impact on the scaledness of song, creating a spectrum from chanted styles to more melodious styles. Overall, the results reveal that speech shows minimal scaledness no matter how it is uttered, and that music's scaledness varies widely depending on its manner of production.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".