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Record W2974126951 · doi:10.1525/mp.2019.37.1.57

Vowel Content Influences Relative Pitch Perception in Vocal Melodies

2019· article· en· W2974126951 on OpenAlexaff
Frank Russo, Dominique T. Vuvan, William Forde Thompson

Bibliographic record

VenueMusic Perception An Interdisciplinary Journal · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBrightnessInterval (graph theory)MelodyPerceptionVowelMathematicsPsychologyPitch contourSpeech recognitionAcousticsAudiologyCommunicationComputer sciencePhysicsOpticsArtMusicalCombinatorics

Abstract

fetched live from OpenAlex

Note-to-note changes in brightness are able to influence the perception of interval size. Changes that are congruent with pitch tend to expand interval size, whereas changes that are incongruent tend to contract. In the case of singing, brightness of notes can vary as a function of vowel content. In the present study, we investigated whether note-to-note changes in brightness arising from vowel content influence perception of relative pitch. In Experiment 1, three-note sequences were synthesized so that they varied with regard to the brightness of vowels from note to note. As expected, brightness influenced judgments of interval size. Changes in brightness that were congruent with changes in pitch led to an expansion of perceived interval size. A follow-up experiment confirmed that the results of Experiment 1 were not due to pitch distortions. In Experiment 2, the final note of three-note sequences was removed, and participants were asked to make speeded judgments of the pitch contour. An analysis of response times revealed that brightness of vowels influenced contour judgments. Changes in brightness that were congruent with changes in pitch led to faster response times than did incongruent changes. These findings show that the brightness of vowels yields an extra-pitch influence on the perception of relative pitch in song.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.957
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.004
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.087
GPT teacher head0.346
Teacher spread0.259 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations16
Published2019
Admission routes1
Has abstractyes

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