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Record W1966997087 · doi:10.1121/1.4787010

Anticipatory motion in piano performance

2006· article· en· W1966997087 on OpenAlexaffabout
Werner Goebl, Caroline Palmėr

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2006
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsPianoMelodyMotion (physics)Computer scienceMovement (music)Motion captureThumbPsychologyArtificial intelligenceAcousticsArtVisual artsMusicalPhysics

Abstract

fetched live from OpenAlex

Recent motor control studies of piano performance address the anticipatory movements of pianists fingers. The reported study investigated anticipatory motion under manipulations of performance rate or tempo. It was tested whether faster tempi require larger anticipatory preparation, as suggested in piano pedagogy literature. Sixteen skilled pianists repeatedly performed short isochronous melodies from memory at four different tempi (from 500 to 143 ms inter-onset intervals). A passive 3-D motion capture system tracked the movements of 40 markers on the hand, fingers, and the piano keys. The melodies manipulated both the repositioning of certain fingers (thumb and pinkie finger) on the keyboard and the distance between repeating finger movements (three or six tones). Functional data analysis techniques were applied to the analysis of motion trajectories. Data analyses are presented that indicate that the repositioning movement of a finger toward its next goal (keypress) begins sooner at faster tempi than at slower tempi in the x plane (sideways motion on the keyboard). Applications to piano practice will be discussed. [Work supported by the Austrian Science Fund, NSERC, and the Canada Research Chairs program.]

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: Empirical · Consensus signal: none
Teacher disagreement score0.595
Threshold uncertainty score0.191

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 head0.011
GPT teacher head0.231
Teacher spread0.220 · 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
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

Citations12
Published2006
Admission routes2
Has abstractyes

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