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Record W2333989994 · doi:10.3813/aaa.918835

Post-Classification of Nominally Identical Steel-String Guitars Using Bridge Admittances

2015· article· en· W2333989994 on OpenAlexfundno aff
Hossein Mansour, Vincent Fréour, Charalampos Saitis, Gary Scavone

Bibliographic record

VenueActa acustica united with Acustica · 2015
Typearticle
Languageen
FieldComputer Science
TopicMusic and Audio Processing
Canadian institutionsnot available
FundersGovernment of Canada
KeywordsGuitarBridge (graph theory)String (physics)AcousticsStructural engineeringEngineeringPhysicsTheoretical physics

Abstract

fetched live from OpenAlex

Dynamic and acoustical measurements were conducted on 18 nominally identical acoustic guitars coming off the same production line and post-classified by the manufacturer as either bassy (i.e., with a more prominent bass response), mid-even (i.e., well-rounded and sounding even from string to string) or treble (i.e., with a brighter sound that cuts through the band). One goal was to find features of the guitar admittances that could be used to automatically classify them according to these categories. A second goal was to investigate whether experienced guitarists agreed with the classifications provided by the manufacturer. Physical properties were investigated independently and in conjunction with perceptual assessments by musicians collected during a classification task. Despite very low agreement across guitarists as well as between musicians and the manufacturer, results showed that the bassy guitars had a lower frequency for their breathing mode. This suggests a lower stiffness-to-weight ratio for the respective guitar bodies, which may be caused by small variations in the plate thickness or wood properties. The guitars characterized as treble in this study tended to have lower averaged mobility in the 600–2000 Hz range, which might suggest a weaker string-to-body coupling at those frequencies and/or a longer decay for higher partials, though these characteristics were not confirmed.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.871
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.070
GPT teacher head0.290
Teacher spread0.221 · 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 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

Citations8
Published2015
Admission routes1
Has abstractyes

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