Can computational music analysis be both musical and computational?
Bibliographic record
Abstract
This special issue of the Journal of Mathematics and Music addresses the topic of computational music analysis. It arose from a series of two international workshops on the topic, one in Berlin and one in Paris, both of which created interesting discussions and debates. In the call for papers, this special issue welcomed previously unpublished contributions that presented computational approaches of any type of music analysis. As a special focus, all papers were asked to analyse the same piece: the first movement of Brahms’ String Quartet No. 1. The aim was to bring together diverse computational analytical approaches and methodologies, such as structural, motivic, semiotic, comparative, reductional, harmonic, transformational, and others, using a variety of computational implementation techniques. By focusing on to the same piece, similarities, differences, and complementarities among the approaches on both the methodological and the analytical results levels could be more easily observed. Authors were particularly encouraged to consider Forte's [Citation1] and Huron's [Citation2] analyses of the string quartet, and relate them to their own work if possible. Three papers were chosen for publication, which reflect the various aspects and levels of computation involved.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".