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Record W2084125432 · doi:10.1121/1.2934963

The guitar as an extension of the voice - Phonetic gestures underlying guitar timbre perception and description

2008· article· en· W2084125432 on OpenAlexaff
Caroline Traube, Maryse Lavoie

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2008
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsTimbreGuitarVibratoGestureSpectral envelopeFormantComputer scienceSpeech recognitionAcousticsString (physics)PerceptionMusical tonePitch (Music)MusicalPsychologyVowelSingingMathematicsArtificial intelligenceArt

Abstract

fetched live from OpenAlex

The guitar is an instrument that gives the player great control over timbre. Different plucking techniques involve varying the finger position along the string and the inclination between the finger and the string. Guitarists perceive subtle variations of these parameters and have developed a rich vocabulary to describe the tones they produce on their instrument. Vocal imitations - onomatopoeias - is another way to intuitively describe instrumental tones. The data that we collected showed that guitar tones can be consistently associated with different types of vowels depending on instrumental gesture parameters, suggesting that guitar tones can evoke "phonetic gestures" as defined in the motor theory of speech perception. In addition, these phonetic gestures seem to be at the origin of a large subset of commonly used verbal descriptors: open, oval, round, thin, closed, nasal, hollow, etc. These analogies can be explained by the comb-filter shaped spectral envelope of plucked-string instruments which emphasizes energy in the region of vocal formants. We conclude that, when technical difficulties have been surmounted, performers can use their musical instrument as an extension of their voice, the musical instruments allowing a virtuosic control of sound parameters similar to those involved in the paralinguistic elements of speech.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.601
Threshold uncertainty score0.918

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 studies0.0010.002
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 head0.031
GPT teacher head0.263
Teacher spread0.232 · 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 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

Citations1
Published2008
Admission routes1
Has abstractyes

Explore more

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