An Empirical Study on the Effectiveness of Feature Selection and Ensemble Learning Techniques for Music Genre Classification
Bibliographic record
Abstract
Classical machine learning has long been utilized for classification and regression tasks, primarily focusing on tabular data or handcrafted features derived from various data modalities, such as music signals. Music Information Retrieval (MIR) is an emerging field that seeks to automate the management process of musical data. This paper explores the potential of employing ensemble learning techniques to enhance classification performance while assessing the impact of feature selection methods on accuracy and computational efficiency across three publicly available datasets: Spotify, TCC_CED, and GTZAN. The Spotify and TCC_CED datasets contain high-level musical features, such as energy, key, and duration, while the GTZAN dataset incorporates low-level acoustic features extracted from audio recordings. The empirical experiments and qualitative analysis reveal a significant performance improvement when employing ensemble learning techniques for handling high-level features. Furthermore, the findings suggest that applying appropriate feature selection methods can substantially reduce computational time. As a result, by strategically combining optimal feature selection and classification models, the performance can be boosted in terms of accuracy and computational time. This study provides insights for optimizing music genre classification tasks through the strategic selection and balancing of model performance, ensemble learning techniques, and feature selection methods, ultimately contributing to advancements of musical genre classification tasks in MIR.
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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 | 0.017 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".