GuitarNeXt: An Advanced Convolutional Neural Network Architecture for Music Genre Classification
Bibliographic record
Abstract
Music genre classification is a challenging task that has been extensively addressed using various deep learning methods.Recently, convolutional neural networks (CNNs) have shown significant promise in this domain.This paper introduces GuitarNeXt, a novel CNN architecture designed specifically for music genre classification.Our approach utilizes a publicly available dataset containing audio recordings and spectral images across multiple genres to evaluate the performance of GuitarNeXt.The architecture of GuitarNeXt includes four primary layers: a stem layer employing patchify convolution to produce initial tensors, a main GuitarNeXt layer that integrates a hybrid attention mechanism with depth concatenation and scaling convolutions, a downsampling layer combining average and maximum pooling with depth concatenation, and an output layer that applies global pooling to produce a feature map for classification.Experimental results demonstrate that GuitarNeXt achieves a classification accuracy of 96.40% and a precision of 96.59% on the test set, highlighting its effectiveness and potential as a robust tool for automated music genre classification.This innovative model not only advances the field of music analysis but also sets a new benchmark for subsequent research in the area.
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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 | 0.000 | 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".