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Record W2276077028 · doi:10.71781/2758

Centor : concept d’interface de spatialisation sonore additive

2014· dissertation· fr· W2276077028 on OpenAlexfundno aff
Simon Mercier-Nguyen

Bibliographic record

VenueOpen MIND · 2014
Typedissertation
Languagefr
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsnot available
FundersUniversité de Montréal
KeywordsInterface (matter)Computer scienceOperating system

Abstract

fetched live from OpenAlex

Cette recherche porte un regard critique sur les interfaces de spatialisation sonore et positionne la composition de musique spatiale, un champ d’étude en musique, à l’avant plan d’une recherche en design. Il détaille l’approche de recherche qui est centrée sur le processus de composition de musique spatiale et les modèles mentaux de compositeurs électroacoustiques afin de livrer des recommandations de design pour le développement d’une interface de spatialisation musicale nommée Centor. Cette recherche montre qu’un processus de design mené à l’intersection du design d’interface, du design d’interaction et de la théorie musicale peut mener à une proposition pertinente et innovatrice pour chacun des domaines d’étude. Nous présentons la recherche et le développement du concept de spatialisation additive, une méthode de spatialisation sonore par patrons qui applique le vocabulaire spectromorphologique de Denis Smalley. C’est un concept d’outil de spatialisation pour le studio qui complémente les interfaces de composition actuelles et ouvre un nouveau champ de possibilités pour l’exploration spatiale en musique électroacoustique. La démarche de recherche présentée ici se veut une contribution au domaine du design d’interfaces musicales, spécifiquement les interfaces de spatialisation, mais propose aussi un processus de design pour la création d’interfaces numériques d’expression artistique.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.932
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0010.000
Open science0.0020.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.035
GPT teacher head0.314
Teacher spread0.280 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2014
Admission routes1
Has abstractyes

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