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Record W3186201616 · doi:10.1121/10.0005587

Perception of violin soundpost tightness through playing and listening tests

2021· article· en· W3186201616 on OpenAlexafffund
Lei Fu, Claudia Fritz, Gary Scavone

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2021
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCentre for Interdisciplinary Research in Music Media and TechnologyChina Scholarship CouncilSchulich School of Music
KeywordsViolinActive listeningPerceptionAudiologyPsychologyMathematicsAffect (linguistics)AcousticsSpeech recognitionCommunicationComputer scienceMedicinePhysics

Abstract

fetched live from OpenAlex

This study involved playing and listening (using recorded sounds) experiments to investigate how changes in soundpost length (for a fixed soundpost position) affect the perceptual qualities of the violin and what the threshold of change is below which players and luthiers do not perceive differences. A length-adjustable carbon fiber soundpost was employed. During the playing experiment, subjects played a provided violin on which the soundpost length was modified by the experimenter to find their optimal soundpost lengths. Then the experimenter varied the soundpost length randomly in ten trials within ±0.11 mm around their optimal lengths and asked subjects to always compare with the previous setting. The results showed that subjects' optimal soundpost lengths varied from 0.132 to 0.616 mm relative to the original length (53 mm), but subjects could not recognize length variation of 0.11 mm or less at above chance levels. During the listening experiment, subjects listened to 16 pairs of recordings through a computer interface and were asked, for each pair, whether the violin setup was the same or different. The results showed that subjects could differentiate soundpost lengths with a difference of about 0.198 mm at better than chance level.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.585
Threshold uncertainty score0.294

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.016
GPT teacher head0.263
Teacher spread0.247 · 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 designBench or experimental
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

Citations3
Published2021
Admission routes2
Has abstractyes

Explore more

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