Analysis and Enhancement Strategies of Emotional Expression Based on Pattern Recognition in Vocal Performance
Bibliographic record
Abstract
The essence of music is the carrier of human emotion expression, with the continuous deepening of music science and technology research, how to realize more accurate music emotion recognition has become the focus of public attention.This paper constructs a music emotion recognition model based on discrete emotion space (WLDNN_SAGAN).After pre-processing the collected audio data of vocal performances, the attention mechanism is introduced to weight and fuse the extracted low-level and middle-high-level music emotion features, and then the fused feature information is inputted into the WLDNN_SAGAN network to classify music emotions.The experimental results show that the model in this paper will improve the recognition accuracy of different emotions.Compared with the comparison model, the accuracy of this paper's model reaches 60% and above on three DIFFERENT datasets.The emotional vein of Chinese folk song performance identified by the model is lightness towards sadness and sacredness, which is consistent with the historical facts of Chinese folk song creation.In conclusion, the emotional expression of vocal performance can be enhanced by understanding the cultural connotation, applying singing techniques and body language.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".