eTu{d,b}e: DEVELOPING AND PERFORMING SPATIALIZATION MODELS FOR IMPROVISING MUSICAL AGENTS
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".