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Record W1991695388 · doi:10.1121/1.2942957

Modification of acoustic-vocal mappings for fundamental frequency control in singers and nonsingers

2007· article· en· W1991695388 on OpenAlexaff
Jeffery A. Jones, Dwayne Keough

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2007
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsSingingSemitoneAuditory feedbackFundamental frequencyAcousticsRepresentation (politics)Control (management)Speech productionAudiologyComputer sciencePsychologySpeech recognitionCommunicationPhysicsMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

Singing requires accurate control of the fundamental frequency (F0) of the voice. Previous work has demonstrated that F0 control involves an interplay between closed- and open-loop control. A frequency-altered auditory feedback (FAF) paradigm was used to examine the acoustic-motor representation of the mapping between F0 feedback and the vocal production system in singers and nonsingers. Participants sang a note while hearing their F0 shifted down one semitone. Both singers and nonsingers compensated for the F0 perturbations by increasing their F0. However, the magnitude of compensations was initially smaller in singers than nonsingers. Moreover, after participants heard their feedback returned to normal, the aftereffects observed for singers were larger than those observed for nonsingers. This pattern of aftereffects generalized to the production of another note different than the one participants produced while hearing FAF. Combined, these observations suggest that singers rely more on an internal model for F0 production during singing than nonsingers.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.886
Threshold uncertainty score0.397

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 head0.016
GPT teacher head0.258
Teacher spread0.242 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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
Published2007
Admission routes1
Has abstractyes

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