A Particle Filter for Predicting an Orchestral Conductor's Baton Movements.
Bibliographic record
Abstract
Telematic musical performance, in which performers at two or more sites collaborate via networked audio and video, suffers significantly from latency.In the extreme case, performers at all sites slow to match their delayed counterparts, resulting in a steadily decreasing tempo.Introducing video of a conductor does not immediately solve the problem, as conductor video is also subjected to network latencies.This article lays the groundwork for an alternative approach to mitigating the effects of latency in distributed orchestral performances, based on generation of a predicted version of the conductor's baton trajectory.The prediction step is the most fundamental problem in this scheme, for which we propose the use of conventional machine learning techniques.Specifically, we demonstrate a particle filter and an extended Kalman filter that each track the location of the baton's tip and predict it multiple beats into the future; we compare these with a conventional feature-based method.We also describe a generic two-part framework that prescribes the incorporation of rehearsal data into a probabilistic model, which is then adapted during live performance.Finally, we suggest a framework and experimental methodology for establishing perceptually based metrics for predicted baton paths.Note that the perceptual efficacy of the presented methods requires experimental confirmation beyond the scope of this article.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".