Melody and Motion: Integrating Guitar Gestures with Musical Patterns for Extended Control in Live Performance and Metaverse Applications
Bibliographic record
Abstract
This paper introduces a novel system for detecting gestural movements of guitarists during live performances, integrated with a pre-existing musical pattern detection framework. The system enables guitarists to trigger peripheral devices through combined musical and physical gestures, extending instrumental control beyond traditional interfaces. We conducted a case study evaluation with five professional guitarists to explore the continuum between natural and theatrical instrument movements, examining how these gestures can be leveraged for expressive control. Technical evaluation of the system using a 500-event test corpus demonstrated a precision of 0.79, recall of 0.76, and F1 score of 0.78 for combined gesture and pattern detection. Through a set of interviews we further investigated practical applications within Musical Metaverse environments, highlighting opportunities for immersive performance experiences. Our findings reveal how combining musical content with physical expression creates intentional and novel performance controls, opening novel possibilities for interactive music that bridges traditional performances with virtual environments.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".