A Mixed Timing Method for Designing Natural Rhythms in Real-Time Media
Bibliographic record
Abstract
Timing is a core expressive material of real-time media. Practitioners regularly need to generate periodic events at controlled paces such as blinking lights or rhythmic sounds. Since strict periodicity often feels artificial and machinic, a common approach consists in adding randomness to create a more organic, less predictable cadence. However, simple jittering approaches that inject noise directly into the period or frequency of a process provide limited control and can distort the expected timing. This report presents a method that overcomes these limitations by generating irregular but statistically reliable event sequences. Based on the Poisson distribution, it preserves the desired long-run event rate while allowing variability to be modulated precisely, yielding rhythms that feel natural without compromising timing accuracy. We compare several approaches and introduce a mixed Poisson model that offers a continuous, intuitive control over randomness, from stable metronome-like pacing to expressive, burst-like irregularity. Practical implementation on embedded systems with limited computational resources is also presented, demonstrating that the method is expressive and lightweight.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".