JUXTAPOSITION BETWEEN TWO CONVOLUTIONAL NEURAL NETWORK FOR MUSIC GENRE CLASSIFICATION
Bibliographic record
Abstract
Music genre classification is the fundamental step involved in building a strong recommendation system. If music classification has to be carried out manually, then one has to listen to numerous songs and then select the genre. This process is not only time-consuming, it is quite a tedious task. The music industry has seen an excellent flow of latest channels to browse and distribute music. This doesn't return without drawbacks. With the increase in data, manual curation has become a difficult task. Audio files have a plethora of features that could be used to make parts of this process a lot easier. Advancements in technology have made it possible to extract the features of audio files. However, the most effective way to handle these for various tasks is unknown. This paper compared the two deep learning models of convolutional neural networks namely Alex-net and Res-net for the purpose of music genre classification using mel-spectrogram images for training. These aforementioned models were tested on GTZAN datasets. It was found that the results showed 56.0% accuracy for the res-net model which was outperformed by alex-net with an accuracy of 80.5%. Keywords: GTZAN Dataset, Alex-net, Res-net, Convolutional Neural Network
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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) | 0.004 | 0.001 |
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".