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Record W2085747923 · doi:10.1525/mp.2009.26.5.439

Movement-Related Feedback and Temporal Accuracy in Clarinet Performance

2009· article· en· W2085747923 on OpenAlexaff
Caroline Palmėr, Erik Koopmans, Janeen D. Loehr, Christine Carter

Bibliographic record

VenueMusic Perception An Interdisciplinary Journal · 2009
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsMcGill University
Fundersnot available
KeywordsKey (lock)KinematicsMovement (music)Synchronization (alternating current)Speech recognitionTone (literature)Computer scienceSensory systemMelodyArtificial intelligenceComputer visionCommunicationPsychologyAcousticsCognitive psychology

Abstract

fetched live from OpenAlex

SENSORY INFORMATION AVAILABLE WHEN MUSICIANS' fingers arrive on instrument keys contributes to temporal accuracy in piano performance (Goebl & Palmer, 2008). The hypothesis that timing accuracy is related to sensory (tactile) information available at finger-key contact was extended to clarinetists' finger movements during key depressions and releases that, together with breathing, determine the timing of tone onsets. Skilled clarinetists performed melodies at different tempi in a synchronization task while their movements were recorded with motion capture. Finger accelerations indicated consistent kinematic landmarks when fingers made initial contact with or release from the key surface. Performances that contained more kinematic landmarks had reduced timing error. The magnitude of finger accelerations on key contact and release was positively correlated with increased temporal accuracy during the subsequent keystroke. These findings suggest that sensory information available at finger-key contact enhances the temporal accuracy of music 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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.966
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
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.323
Teacher spread0.285 · 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.

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

Citations43
Published2009
Admission routes1
Has abstractyes

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