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Record W4391104891 · doi:10.17501/23572744.2023.10102

eTu{d,b}e: DEVELOPING AND PERFORMING SPATIALIZATION MODELS FOR IMPROVISING MUSICAL AGENTS

2024· article· en· W4391104891 on OpenAlexafffund
Kasey Pocius, Thomas A. Davis, Vincent Cusson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsMcGill UniversityCentre for Interdisciplinary Research in Music Media and Technology
FundersSimon Fraser UniversitySocial Sciences and Humanities Research Council of CanadaCanada Council for the ArtsNatural Sciences and Engineering Research Council of CanadaCentre for Interdisciplinary Research in Music Media and TechnologyMcGill University
KeywordsSpatializationImprovisationComputer scienceMusicalMultimediaArtVisual arts

Abstract

fetched live from OpenAlex

The eTube is a simple acoustic instrument outfitted with a microphone and a two-button controller which we program to facilitate interaction between an improviser and improvising musical agents.Through an iterative research-creation process, we develop and perform various etudes with the eTube and musical agents, generating new knowledge through musical creation.Through this process, we have developed two interactive spatialization systems for the eTube.We begin by describing the eTu{d,b}e framework which refers to the eTube instrument and a series of improvised etudes based on human-computer musical interactions.An overview of the instrument is presented, as well as the existing systems as they were when Pocius began the spatialization portion of this project.Secondly, we outline two interactive spatialization systems designed for improvised performance with the eTube and musical agents.An overview of the updates made to the eTu{d,b}e framework is presented, followed by a description of the two spatialization systems.Finally, six different performances are presented as case studies to emphasize the advancements and challenges of the project as new approaches are developed.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.912
Threshold uncertainty score0.239

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.044
GPT teacher head0.285
Teacher spread0.241 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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
Published2024
Admission routes2
Has abstractyes

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