Classification of Indonesian Music Genres Using Transfer Learning with ResNet-50 and Mel-Frequency Cepstral Coefficient Feature Extraction
Bibliographic record
Abstract
Indonesian music features diverse genres such as pop, dangdut, keroncong, and campursari, each with distinct characteristics and audiences.The increasing digitalization of music necessitates automated systems for accurate genre classification.This study develops a deep learning-based classification system using Convolutional Neural Networks (CNNs), specifically ResNet-50 and VGG-16, with Mel-Frequency Cepstral Coefficients (MFCCs) for feature extraction.MFCCs effectively capture music's frequency spectrum, making them suitable for genre classification.Experimental results show that ResNet-50 outperforms VGG-16, achieving 99% accuracy, 98% precision, 98% recall, and a 98% F1-score with an 80:20 data split.ResNet-50's residual connections enable better feature learning and mitigate gradient vanishing, leading to superior performance.VGG-16 also performed well but exhibited slightly lower accuracy due to its deeper structure without residual connections.The study emphasizes the impact of dataset size, showing that a larger training set improves generalization.However, limitations such as the relatively small dataset and the exclusive use of MFCCs may affect performance on unseen data.Future research should explore larger datasets, additional Indonesian genres, and hybrid feature extraction techniques.
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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 | 0.000 | 0.000 |
| 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".