MULTI-DIMENSIONAL SYNCHRONIZATION FOR RHYTHMIC SONIFICATION
Bibliographic record
Abstract
Human locomotion is fundamentally periodic, so when sonifying gait, it is desirable to exploit this periodicity to produce rhythmic sonification synchronized to the motion. To achieve this rhythmic sonification, some mechanism is required to synchronize an oscillator to the period of the motion. This paper presents a method to synchronize to multidimensional signals like those produced by a motion capture system. Using a subset of the joint-angle signals produced by motion capture, the method estimates the phase of a periodic, multidimensional model to match data observed from a moving subject. It does this using an optimization algorithm applied to a suitable objective function. We demonstrate the synchronization with data from a publicly available motion capture database, producing sonifications of drum beats synchronized to footfalls of subjects. The method is robust and shares some common features of phase-locked loops used for synchronizing onedimensional sinusoidal signals. We foresee applications to sonification for athletics and clinical treatment of gait disorders. 1.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.004 | 0.001 |
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".