Exploration on Classification of Vocal Music Theme Based on Intelligent Multi Image Feature Fusion
Bibliographic record
Abstract
Music is a form of artistic expression, which can cultivate sentiment. Music can also be regarded as a language. Listening to music can give play to imagination through hearing, so as to resonate with the expression ideas of music creators. Music has no boundaries and is a cultural heritage. Therefore, when listening to music, music itself is not the purpose of listening, but the meaning behind music is the focus of feeling. People can communicate and express their feelings through music. The scope of music is very broad. Vocal music, as a kind of music, can be seen as an art combined with language. People’s understanding of the classification of the main melody of vocal music is very simple. The music culture is broad and profound. The clear classification of the main melody of vocal music is of great significance to deepen the understanding of music. Therefore, this paper proposed an intelligent multi image feature fusion classification of vocal music theme. Through the experiment on the classification of vocal music theme by the artificial intelligence multi image feature fusion model, the data obtained showed that the number of single part music included in the 60 music classification was 17; the number of polyphonic music was 22, and the number of theme music was 21. It was consistent with the judgment results of two professional music teachers and student B, which indicated that intelligent multi image feature fusion can improve the accuracy of music classification. This study provided a reference value for the role of intelligent multi image feature fusion in the classification of vocal theme, and provided a future direction for the development of vocal theme classification.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".