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 foundational concept of Western music, serving as the basis for chords and harmonies, detecting and assessing cadences and form, and conveying musical emotion. Traditionally treated categorically, here we build upon recent work exploring this crucial musical construct on a continuum, an approach we refer to as ‘relative mode’. Specifically, we formulate and evaluate a computational model calculating this property from either symbolic or audio representations of music by adapting common key-finding techniques traditionally used to identify mode categorically. Here, we use them to infer the relative mode based on differences between the potential strength of major and minor key candidates. The model evaluation is based on a corpus of excerpts from Preludes by Bach, Chopin, and Shostakovich previously assessed by expert music analysts. Our results suggest that the model (using only audio files) is able to predict relative mode to a degree closely aligning with 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 | 0.008 | 0.025 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".