Generative Music with the Living Machine: Using Rule-Based Improvisation to Generate Narrative and Soundtrack
Bibliographic record
Abstract
This paper proposes the term generative improvisation to describe free improvisations that are constrained by a set of human-determined limits. The purpose of this term is to emphasize the way that rules limit performer choices and define the meanings of different musical gestures to generate specific kinds of performances. By examining Narrative Generator (for Living Machine), a rule-based improvisation for narrator, instruments, and electronics, I demonstrate that generative improvisation can be used to produce a performance of a coherent improvised narrative and soundtrack by basing the rules of the work on the theories and practices employed in film and video games. From there, the paper examines the relationships between different performers, the performers and the composer, and the composer, performer, and audience in the work, discussing the potential impacts of these relationships. Finally, I consider the role accessibility plays in spreading these potential impacts to a broader audience.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".