Does Vibrato Define Genre or Vice Versa? A Novel Parametric Approach to Vocal Vibrato Analysis
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: A multifactorial phenomenon, vibrato exists in a variety of musical styles and genre contexts. Current vocal vibrato analysis methods using average metrics are applicable only if the vibrato is uniform, consistent, persistent, and omnipresent; features belonging predominantly to the Western Classical Opera esthetic. Historically, vocal vibrato has been analyzed with tools presuming this lens, disregarding significant stylistic characteristics of many other genres with nonnormative, naturally occurring vibrato features. Therefore, a new system of vibrato parameters considering vibrato regularity, variability, and stability over time in more genres is essential. METHODS: band-pass filtering, and a fast fourier transform long term average spectrum in Praat. Correlations in mean half-extent (in cents), pitch, vowel, and style/singer subject were analyzed for each sample and assessed using standard deviation, coefficient of variation (CV), linear and polynomial regression, and non-linear regression techniques in R. A subsequent perceptual survey using samples most representative of each genre's average CV was distributed to seven vocal pedagogue judges. RESULTS: The acoustic analysis results indicated that vibrato variability predictably distinguished performed genres. The CV well-characterized vibrato variability and was higher in the samples of Musical Theater and Jazz singers. A 4-parameter logistic regression model is proposed as a novel application and more accurate representation of such multiphasic vibrato with complex shapes. The perceptual survey results confirmed that genre may be accurately distinguished and classified based on the most representative vibrato variability for each group, though Musical Theater singers' vibrato was more challenging to categorize compared with Opera and Jazz singers' vibrato. DISCUSSION: The novel application of perceptually correlated vibrato models and time-varying parameters proposed in this two-part study may be employed to examine and evaluate complex vibrato patterns and style-specific performance. In turn, this promotes more genre-inclusive voice training in the vocal studio and contributes ecologically valid normative thresholds for vibrato habilitation and rehabilitation in the voice clinic.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".