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Record W2024807760 · doi:10.1121/1.4786614

Real-time measurement/viewing of vocal-tract influence during wind instrument performance

2006· article· en· W2024807760 on OpenAlexaff
Gary Scavone

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2006
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsMcGill UniversityCentre for Interdisciplinary Research in Music Media and Technology
Fundersnot available
KeywordsAcousticsVocal tractObserver (physics)Computer scienceMechanism (biology)Upstream (networking)PhysicsTelecommunications

Abstract

fetched live from OpenAlex

Since the early 1980s, there have been a number of investigations into the role and influence of a player’s vocal tract on the sound production of wind instruments. While the underlying acoustic principles are relatively well understood, a general lack of agreement remains within both the music acoustics and performance communities with regard to the importance of this mechanism during playing conditions. Disparities arise in part because subtle manipulations of the oral cavity can affect the response of the instrument without necessarily producing effects audible to an observer. A real-time measurement system is demonstrated that provides a visual comparison of the relative strengths of the ‘‘upstream’’ windway and ‘‘downstream’’ air column impedances under playing conditions. The system assumes continuity of volume flow on either side of the ‘‘reed,’’ which leads to a direct proportionality between the upstream and downstream pressures and impedances. Playing experiments clearly demonstrate many instances in which vocal-tract manipulations can cause impedance peak magnitudes in the mouth cavity to exceed those in the downstream air column. In addition to providing visual ‘‘proof’’ of such manipulations, the system is expected to offer a pedagogical tool to help performers better learn how to make use of this control mechanism.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.763
Threshold uncertainty score0.270

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.217
Teacher spread0.204 · 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

Citations2
Published2006
Admission routes1
Has abstractyes

Explore more

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