Major-minorness in Tonal music -- Evaluation of Relative Mode Estimation using Expert Ratings and Audio-Based Key-finding Principles
Bibliographic record
Abstract
Mode is a fundamental concept in Western music theory, as well as many aspects of music perception. It is foundational to tasks ranging from identifying chords, detecting cadences, and assessing form, as well as the encoding/decoding of musical emotion and expression. Here we expand the categorical notion of mode as major or minor to a continuum, an approach we refer to as relative mode. We formulate and evaluate a computational model that calculates this property from either symbolic or audio representations of music. Building on common key-finding techniques created to identify mode in a categorical manner, here we use them to infer the relative mode based on the difference between the potential key candidate strengths in major and minor keys. The model evaluation is based on a corpus consisting of excerpts from Preludes by Bach, Chopin, and Shostakovich previously assessed by expert music analysts. Our results suggest that the model is able to predict the relative mode to a degree that closely matches evaluations by experts using both audio and notated scores. A pragmatic set of parameters for the model is identified and the shortcomings and the applicability of the model to other eras and genres are discussed
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".