MétaCan
Menu
Back to cohort
Record W2231586481

The relationship between vocal abilities and singing accuracy

2012· article· en· W2231586481 on OpenAlexaff
Pauline Larrouy-Maestri, Sean Hutchins, Isabelle Peretz

Bibliographic record

VenueOpen Repository and Bibliography (University of Liège) · 2012
Typearticle
Languageen
FieldEngineering
TopicVehicle Noise and Vibration Control
Canadian institutionsUniversité de MontréalInternational Laboratory for Brain, Music and Sound Research
Fundersnot available
KeywordsSingingPsychologySpeech recognitionCommunicationAudiologyCognitive psychologyAcousticsComputer scienceMedicinePhysics
DOInot available

Abstract

fetched live from OpenAlex

Poor-pitch singing could be caused by poor pitch perception or poor vocal-motor control abilities. This study aims to contrast these two possible causes in order to determine the role of vocal control on the accuracy of sung performances among nonmusicians. Participants matched recordings of their own voices either by singing, or by manipulating those recordings on a physical instrument which could control the pitch of the vocal recording playback by sliding the finger along a position sensor. In addition, participants sang a full song from memory. Overall, participants were more accurate at matching the pitch of the original recording with the instrument than with their voice. In addition, singers who were more accurate at vocal pitch matching tended to have better vocal quality (as assessed through standard measurements, e.g. jitter, stability), and were better at singing whole songs. This pattern of results confirms that vocal-motor control, rather than pitch perception ability, is the primary driver of singing ability, and provides insight into the relationship between pitch accuracy and vocal quality.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.470

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.027
GPT teacher head0.233
Teacher spread0.206 · 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 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

Citations0
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueOpen Repository and Bibliography (University of Liège)Same topicVehicle Noise and Vibration ControlFrench-language works237,207