The effect of <i>chiaroscuro</i> and <i>coup de glotte</i> training on reducing the effects of intrinsic pitch in sung vowel transitions
Bibliographic record
Abstract
Intrinsic pitch is an acoustic value that differs depending on vowel height. Sapir (1989) describes the various theories that provide an explanation for intrinsic pitch. Essentially, vowels which are produced higher in the filter (e.g., /i/ and /u/) tend to be pitched slightly higher than vowels produced at lower locations in the filter (e.g., /a/). The purpose of this study is to determine whether the chiaroscuro and coup de glotte vocal techniques used in the bel canto method of singing reduce the natural effects of intrinsic pitch of sung vowels by attempting to equalize both resonance and vibrational properties across the vowel spectrum. Six singers (2 sopranos, 2 tenors, and 2 basses) were recorded in this study. Participants sang a set of vowel transition exercises in a pre-training setting, then were trained in chiaroscuro and coup de glotte techniques, after which they did a post-training recording. Pitch, formant values, and amplitude (as mean values and at various times within each vowel) were extracted for a before versus after comparison. The results will be discussed in relation to how this technique might be helpful in reducing the effect of intrinsic pitch on sung vowels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".