Bibliographic record
Abstract
Online music streaming has become extremely ubiquitous amongst society. In the last year, we have seen over 600 million individuals subscribe to a music streaming platform. With user experience playing a large role in purchasing and trusting a product, music genre classification becomes ever more important. Music genres provide streaming services a way to recommend new music to its users which can serve to improve their overall user experience. With the amount of music added to these platforms every day automatic music genre classification continues to be a focal point of research. Therefore, our objective is to contribute to the literature research of automatic music genre classification. In this study, we seek to evaluate the performance of a kNN music genre classifier with an increasing number of target genre classes. Our methodology involves training and testing five kNN classifiers each with an increasing number of target genres. From these tests we evaluate the performance of each model using metrics such as accuracy, precision, and recall. The results of this study suggest that the performance of a kNN classifier decreases with an increase in the number of target classes. The highest accuracy model is the classifier with two target classes at an accuracy of 94% while the least accurate model contains six target classes with an accuracy of 55%.
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.
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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".