Negotiated Content: Generative Soundscape Composition by Autonomous Musical Agents in Coming Together: Freesound.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Generative music systems have been successful in styles and genres where there are explicit rules that can be programmed into the system. Practices and procedures within soundscape composition have tended to be implicit, in which recordings are selected, combined, and processed based upon contextual relationships. We present a system – Coming Together: Freesound – in which four autonomous artificial agents choose sounds from a large pre-analyzed database of soundscape recordings (from freesound.org), based upon their spectral content and metadata tags. Agents analyze, in realtime, other agentʼs audio, and attempt to avoid dominant spectral areas of other agents by selecting sounds that do not mask other agent’s spectra. Furthermore, selections from the database are constrained by metadata tags describing the sounds. Example compositions have been evaluated through subject testing, comparing them to human-composed compositions, and the results are discussed.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it