Reactive Environment For Network Music Performance
Bibliographic record
Abstract
For a number of years, musicians in different locations have been able toperform with one another over a network as though present on the same stage.However, rather than attempt to re-create an environment for Network MusicPerformance (NMP) that mimics co-present performance as closely as possible, wepropose focusing on providing musicians with additional controls that can helpincrease the level of interaction between them. To this end, we have developeda reactive environment for distributed performance that provides participantsdynamic, real-time control over several aspects of their performance, enablingthem to change volume levels and experience exaggerated stereo panning. Inaddition, our reactive environment reinforces a feeling of a ``shared space''between musicians. It differs most notably from standard ventures into thedesign of novel musical interfaces and installations in its reliance onuser-centric methodologies borrowed from the field of Human-ComputerInteraction (HCI). Not only does this research enable us to closely examine thecommunicative aspects of performance, it also allows us to explore newinterpretations of the network as a performance space. This paper describes themotivation and background behind our project, the work that has been undertakentowards its realization and the future steps that have yet to be explored.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".