Vowel Formant Structure Predicts Metric Position in Hip-hop Lyrics
Bibliographic record
Abstract
In order to be heard over the low-frequency energy of a loud orchestra, opera singers adjust their vocal tracts to increase high-frequency energy around 3,000 Hz (known as a “singer's formant”). In rap music, rhymes often coincide with the beat and thus may be masked by loud, low-frequency percussion events. How do emcees (i.e., rappers) avoid masking of on-beat rhymes? If emcees exploit formant structure, this may be reflected in the distribution of on- and off-beat vowels. To test this prediction, we used a sample of words from the MCFlow rap lyric corpus (Condit-Schultz, 2016). Frequency of occurrence of on- and off-beat words was compared. Each word contained one of eight vowel nuclei; population estimates of each vowel's first and second formant (F1 and F2) frequencies were obtained from an existing source. A bias was observed: vowels with higher F2, which are less likely to be masked by percussion, were favored for on-beat words. Words with lower F2 vowels, which may be masked, were more likely to deviate from the beat. Bias was most evident among rhyming words but persisted for nonrhyming words. These findings imply that emcees use formant structure to implicitly or explicitly target the intelligibility of salient lyric events.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".