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Record W2897639213 · doi:10.1121/1.5068287

How different strings affect violin qualities

2018· article· en· W2897639213 on OpenAlexaff
Lei Fu, Gary Scavone, Claudia Fritz

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2018
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsMcGill UniversityCentre for Interdisciplinary Research in Music Media and Technology
Fundersnot available
KeywordsViolinAcousticsQuality (philosophy)Set (abstract data type)Computer scienceSound (geography)Sound qualityPerceptionPsychologyPhysics

Abstract

fetched live from OpenAlex

The brand and model of strings used on violins are considered to play a significant role in their playability and sound quality. An experiment was designed to test the perceptual quality of different violin strings. A professional violinist selected two violins, from a set of the same make/model, that had similar sound and playing qualities. Three different types of strings were chosen for this study: Dominant, Kaplan, and Pro-Arté strings. Professional and advanced student violinists were invited to play and evaluate the violins. The experiment involved three phases: in the first phase, the two violins were strung with the same types of strings; in the last two phases, the strings of one of the violins was changed to the other two different types of strings (in a random manner). Violinists were asked to freely describe the differences of the two violins in each phase and rate them on eight specified criteria. Preliminary results indicate that the violin with Dominant strings was perceived as being more responsive and having a brighter sound, while that with the Pro-Arté strings was considered to have a richer sound and better overall 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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.860
Threshold uncertainty score0.600

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.018
GPT teacher head0.258
Teacher spread0.239 · 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 designNot applicable
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

Citations1
Published2018
Admission routes1
Has abstractyes

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