Virtual Gesture Control and Synthesis of Music Performances: Qualitative Evaluation of Synthesized Timpani Exercises
Bibliographic record
Abstract
The increasing availability of software for creating real-time simulations of musical instrument sounds allows for the design of new visual and sounding media. These past decades have especially focused on the control of real and virtual instruments by natural gestures. In this paper, we present and extensively evaluate a framework (Figure 1) for the control of virtual percussion instruments, by modeling and simulating virtual percussionists gestures. By positioning the virtual performer at the center of the gesture-sound synthesis system, we aim at providing original tools to analyze and synthesize instrumental gesture performances. Our physics-based approach for gesture simulation brings some insight into the effect of biomechanical parameters of the gesture on the instrumental performance. Simulating both gesture and sound by physical models leads also to a coherent and human-centered interaction and provides new ways of exploring the mapping between gesture and sound. The use of motion capture data enables the realistic synthesis of both pre-recorded and novel percussion sequences from the specification of gesture scores. Such scores involve motion editing techniques applied to simple beat attacks. We therefore propose an original gesture language based on the instrumental playing techniques. This language is characterized by expressivity, interactivity with the user, and the possibility to take into account co-articulation between gesture units. Finally, providing 3D visual rendering synchronized with sound rendering allows us to observe virtual performances to the light of real ones, and to qualitatively evaluate both pedagogical and compositional capabilities of such a system.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".