Enhancing multi-label music genre classification through ensemble techniques
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the field of Music Information Retrieval (MIR), multi-label genre classification is the problem of assigning one or more genre labels to a music piece. In this work, we propose a set of ensemble techniques, which are specific to the task of multi-label genre classification. Our goal is to enhance classification performance by combining multiple classifiers. In addition, we also investigate some existing ensemble techniques from machine learning. The effectiveness of these techniques is demonstrated through a set of empirical experiments and various related issues are discussed. To the best of our knowledge, there has been limited work on applying ensemble techniques to multi-label genre classification in the literature and we consider the results in this work as our initial efforts toward this end. The significance of our work has two folds: (1) proposing a set of ensemble techniques specific to music genre classification and (2) shedding light on further research along this direction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it