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Record W2083220765 · doi:10.1525/mp.2012.30.2.147

The Vocal Generosity Effect: How Bad Can Your Singing Be?

2012· article· en· W2083220765 on OpenAlexaff
Sean Hutchins, Catherine Roquet, Isabelle Peretz

Bibliographic record

VenueMusic Perception An Interdisciplinary Journal · 2012
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsUniversité de MontréalInternational Laboratory for Brain, Music and Sound Research
Fundersnot available
KeywordsMelodyTimbreMistuningGenerositySingingTone (literature)ViolinPsychologyContext (archaeology)Speech recognitionAcousticsAudiologyComputer scienceLinguisticsArtPhilosophyMusicalHistoryPhysicsVisual arts

Abstract

fetched live from OpenAlex

prior work indicates that listeners may be more likely to call a note in-tune when it is sung than when it is in another timbre. The current study seeks to confirm whether this vocal generosity effect generalizes to melodies. Musicians and nonmusicians listened to pairs of single tones and scale-based melodies performed with the voice or the violin. The final note was varied in how well it was tuned to the prior context, and for each example, listeners judged whether the final note was intune or not. A strong vocal generosity effect was found for musicians and nonmusicians in both melodic and single tone conditions – a higher degree of mistuning was necessary for listeners to decide that sung tones were out-of-tune compared with violin notes. These results confirm the role of timbre in tuning judgments, and help explain why singers are typically less well-tuned than instrumentalists in performance.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.042
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.066
GPT teacher head0.341
Teacher spread0.275 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations67
Published2012
Admission routes1
Has abstractyes

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