Bibliographic record
Abstract
Abstract Motion capture (mocap)—the recording of three-dimensional movement using high-accuracy systems—has become a standard research tool in the analysis of music performances in the last two decades. A variety of systems is currently available, ranging from optical, multi-camera (passive and/or active) infra-red systems and inertial systems (using orientation sensors) to electromagnetic trackers providing six degrees-of-freedom (DoF) measurement per marker/sensor. Recent advances in technology have made many of these systems more affordable, allowing access to a large research community. Music-related mocap applications include the tracking movements of solo or group, beginner, or expert performers and instruments for teaching performance skills, comparing movement strategies across performers, generating movement synthesis parameters in animation, and use in real-time music interaction. This chapter introduces the basic concepts behind motion capture, reviews the most common mocap technologies used in the study of music performance, and presents several examples of research, pedagogy, and artistic uses. Mocap of single acoustic instrument performances is reviewed, including violin, cello, piano, clarinet, timpani, and acoustic guitar, as well as examples of mocap of multiple instruments. Finally, we discuss the limitations of mocap and possible solutions to overcome them.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".