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Record W2345348734 · doi:10.1121/1.4950132

The effect of <i>chiaroscuro</i> and <i>coup de glotte</i> training on reducing the effects of intrinsic pitch in sung vowel transitions

2016· article· en· W2345348734 on OpenAlexaff
Grace H. Crosby, Benjamin V. Tucker, Laurier Fagnan

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2016
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFormantVowelSingingAcousticsMathematicsCantoDegree (music)Speech recognitionAudiologyPhysicsComputer scienceLinguisticsMedicine

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
Threshold uncertainty score0.601

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.222
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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