Research on Virtual Scene Creation and Interactive Learning Model for Music Performance Combining Virtual Reality and Computer Vision Technology
Bibliographic record
Abstract
With the development of virtual reality and computer vision technology, the demand for virtual scenes of music performances is becoming more and more prosperous, which brings new development opportunities for music performances and music teaching.In this paper, we use the beam leveling method to determine the camera parameters in the virtual scene, implement the calibration process and parameter solving for the camera, and implement the segmentation process for the virtual scene image through the GrabCut algorithm, formulate the model constraints and objective function, construct a virtual scene for music performance, and design a virtual scene system for music performance.Based on the virtual scene of music performance, the interactive learning model of music is proposed, and the virtual roaming mode is formulated by combining human-computer interaction technology to realize the interactive learning roaming of music learners in the virtual scene of music performance.The PSNR and SSIM values of the music performance virtual scene constructed by this paper's technology are 25.8291db and 0.9396 respectively, which are higher than those of the virtual scene construction algorithms such as VSRS and JTDI as a comparison.Carrying out music teaching experiments, the experimental class that applies the interactive learning model of this paper for music interactive learning roaming has higher mean values of all dimensions than the control class in both music learning ability and music listening ability, showing significant differences (P<0.05).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".