Automatic Music Genre Classification based on CNN and LSTM
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Various applications of machine learning are discovered and receiving more and more attention contemporarily. The music industry has benefited from the incorporation of artificial intelligence, especially the field of music classification, as machines are able to organize big data in a more efficient manner than the traditional human expertises. This paper compares two machine learning models, the Convolutional Neural Network model (CNN), and the Long Short Term Memory model (LSTM), from their architectures, functionality, to classification accuracy based on empirical data. The models were trained on two datasets, GTZAN and FMA. The result indicates that the CNN model achieved a 56.0% and 50.5% accuracy for the two datasets respectively, outperforming the LSTM model, which had a 42.0% and 33.5% accuracy. The paper aims to analyze the two models’ capability for music classification and determine which model is better suited for the task. These results shed light on guiding further exploration of computer music.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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 it