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 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 | 0.001 | 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".