Research on optimizing vocal singing posture based on image recognition technology
Bibliographic record
Abstract
Vocal singing is a key art form of many stage singing arts, specifically including acting and singing.The study firstly is to introduce the detection principle of YOLOv5 target detection algorithm, on the basis of which the original YOLOv5 algorithm is improved by reconstructing the backbone network with the use of SENet and GhostNet, then the original YOLOv5 algorithm and the improved YOLOv5 algorithm are tested for comparison, and the test results show that on the target detection dataset Precision, Recall and mAP values reach 85.75%, 72.34% and 78.48% respectively, which are all improved compared with the original algorithm.Secondly, a high-resolution human posture estimation network incorporating multiple attention mechanisms is proposed to further extract multi-scale feature information and global feature information, and validated on publicly available datasets, CDLNet has an AP value of 0.662 and an AR value of 0.731 on the vocal singing posture estimation dataset, comparing with similar methods, the method has an MPJPE in Human3.6MThe lowest is 44.6, which is suitable for use in vocal singing posture estimation in vocal singing scenarios.Finally, an action recognition model based on multi-granularity spatio-temporal graph convolutional neural network designed in this paper is used to analyze the singing gesture action recognition for singing action categories, and experiments show that the average recognition rate of MGstgcn can reach 86.5% on the HSiPu2 dataset, which meets the demand of vocal singing gesture action recognition analysis.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".