Deep Learning Algorithm Based Pattern Recognition of Musical Melodies and Its Effectiveness Optimization in Education
Bibliographic record
Abstract
In order to solve the shortcomings of the sound source separation method, this paper proposes a melody extraction method based on saliency and improved joint neural network, constructs the pitch saliency feature function according to the idea of harmonic energy superposition, pre-processes the audio, and then builds the joint neural network based on Res-CBAM according to the idea of joint neural network of music detection and pitch estimation classification to realize the melody pitch contour tracking. In addition, the calculation of the significance function is introduced to highlight the pitch significance features, so that the graphs input to the neural network have clearer melodic features. The results show that before and after the suppression of the accompaniment, the difference in the time-domain waveforms is not significant in the treble range, but there is a significant difference in the low-frequency range. In addition, the OA accuracy of the Res-CBAM algorithm proposed in this paper is up to 41.14% higher than other algorithms (P < 0.05), and the accuracy of the model is good. Applying this recognition model to teaching found that teaching with this model can significantly improve the subjects' perception of music (t=.197, p=0.002<0.05). It can be seen that the application of the Res-CBAM algorithm to actual music teaching is of great practical importance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".