Research on Pattern Generation and Structure Optimization of Neural Network Algorithms in AI Music Composition
Bibliographic record
Abstract
Artificial Intelligence AI composition is one of the hot topics that have been debated in recent years.In this paper, we first extract monophonic and chordal features from MIDI digital music files.Then the WaveNet intelligent music generation model is used as a carrier to optimize its multilayer convolutional network structure.The audio files are fed into the optimized WaveNet model, and the final training parameters are obtained after several rounds of iterative training.After the model completes the training, music sequences are automatically generated.The results show that the optimized WaveNet model for training leads to a significantly higher accuracy rate in the validation set than before optimization.Compared to other models, the method in this paper generates music using a larger variety of notes, improving the quality of the music theory and chord aspects.Compared with the composite scores of human compositions, the percentage of WaveNet model compositions with scores of 4 and above is about 20.3%, and the percentage of scores of 3 and above is 30.5%.Therefore, the overall level of the compositions generated by the model in this paper is good.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".