Creating systems for collaborative network-based digital music performance.
Bibliographic record
Abstract
The internet has proven to be an important catalyst in bringing together musicians for remote collaboration and performance. Existing technologies for network audio streaming possess varying degrees of technological transparency with regard to allowable bandwidth, latency, and software interface constraints, among other factors. In another realm of digital audio, the performance of “laptop music” presents a set of challenges with regard to human-computer and interperformer interaction—particularly in the context of improvisation. This paper discusses the limitations as well as newfound freedoms that can arise in the construction of musical performance systems that merge the paradigms of laptop music and network music. Several such systems are presented from personal work created over the past several years that consider the meaning of digital music collaboration, the experience of sound-making in remote physical spaces, and the challenge of improvising across time and space with limited visual feedback. These examples include shared audio processing over high-speed networks, shared control of locally generated sound synthesis, working with artifacts in low-bandwidth audio chat clients, and the use of evolutionary algorithms to guide group improvisations.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".