The Symbolic vs. Numeric Controversy in Automatic Analysis of Music
Bibliographic record
Abstract
This position paper describes two families of approaches to automatic analysis of tonal music: approaches based on purely numerical computations, and approaches based on symbolic models. Both approaches have been used in our team with great success, but they address different levels of difficulties, and aim at analyzing different corpus of musical material. Indeed, they are difficult to reconcile and use in a single collaborative environment. We claim that finding ways to integrate smoothly both approaches is the only real bottleneck to producing reliable automatic harmonic analyzers. 1. Introduction Humdrum [Huron, 1994] is a set of general-purpose software tools developed at university of Waterloo (Canada), intended to assist researchers in posing and answering research questions. Humdrum is one of the most ambitious attempts so far at providing computational power to musicians in order to perform complex analysis of musical pieces. In the Faq file of the Humdrum package, one can r...
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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 | 0.015 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".