Research on the efficacy of music aesthetic education on college students’ self-identity enhancement based on neural network modeling
Bibliographic record
Abstract
In this paper, the entire chord progression is added to the generation process through a bidirectional LSTM model, and the Skip-connection method is used to accelerate the convergence speed in all recursive layers except the first one. Different musical emotions are classified based on the Hevner emotion model, and features such as pitch, duration, and tempo of musical emotions are parameterized. The forward neural network is used to construct the music emotion classification model, and the gradient descent learning algorithm is used to algorithmically control the forward neural network model. At the same time, this paper explains the significant enhancement of college students’ self-identity by music aesthetic education based on neural network model from two perspectives: theoretical research and empirical analysis. The results show that the music generation and music emotion classification models constructed based on the neural network algorithm in this paper show good performance in the experiments. After applying the neural network model containing music generation and music emotion classification to music aesthetic education and counseling college students on self-identity, the mean score of self-identity scale of students in the experimental group increased from 50.83 to 88.56, with an improvement of 75.78%, and the results were significant at the 1% level. The effectiveness of this paper’s method in enhancing college students’ self-identity is fully demonstrated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".