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Record W2024431600 · doi:10.1121/1.2934965

Perception, verbal description and gestural control of piano timbre

2008· article· en· W2024431600 on OpenAlexaff
Caroline Traube, Michel Bernays, Madeleine Bellemare

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
KeywordsTimbrePianoArticulation (sociology)GesturePerceptionMeaning (existential)OnomatopoeiaVocabularyComputer scienceObject (grammar)Dynamics (music)PsychologyCognitive psychologySpeech recognitionAcousticsLinguisticsMusicalArtificial intelligenceArtVisual arts

Abstract

fetched live from OpenAlex

Musical expressivity in virtuosic pianistic performance relies heavily on timbre and performers call upon a vast vocabulary to describe the nature of their sound; examples of adjectives include velvety, metallic, bright, round and dark. The present study aims to determine whether this vocabulary, its perceptual meaning and the gesture applied to obtain the sounds it describes, stand as consensual among pianists. The relations between timbre, articulation, register and dynamics are also examined. Nearly 100 verbal descriptors were collected as well as the description of the associated gestures. Some timbres are specific to certain dynamic levels and others are the result of a combination of at least two sonic elements into one resulting sound object, where articulation, - the relation between one note to the next - plays a crucial role. A subset of these adjectives has been selected for further study. A professional pianist performed, on a computer-controlled recording acoustic piano, three short project-designed pieces with several timbres, as designated by adjectives. The excerpts were also captured with microphones to serve as stimuli for a timbre recognition task, both in free form and by selection, to which a group of 17 pianists performed with great accuracy.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.683
Threshold uncertainty score0.483

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 head0.013
GPT teacher head0.218
Teacher spread0.205 · 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

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