Random Walks on Neo-Riemannian Spaces: Towards Generative Transformations
Bibliographic record
Abstract
Random walks, fractional Brownian motion and stochastic processes have been used extensively by composers such as Iannis Xenakis and others, creating instantly recognizable textures. A trained ear can differentiate a uniform random walk from a Poisson process or an fBm process and random rotations. In the opera Sophocles: Antigone by one of the authors of this paper, random walks on neo- Riemannian PLR spaces were experimented with yielding mixed impressions of process music and post-romantic chromaticism. When the random walk is steered by transformational rules, special textures and harmonies emerge. We propose a new kind of parameterizable random walks, a generative system, on a space of arbitrary length chords equipped with an arbitrary distance measure steered from a customizable corpus learned by the system. The corpus provides a particular texture and harmony to the generative process. The learned neo-Riemannian spaces equipped with some distance measure provide the transformational rule base of the concatenative synthesis process.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".