Metric Accent Affects Perception of Key Center in Pop-Music Chord Loops
Bibliographic record
Abstract
Looped chord progressions in popular music can often be heard as having multiple plausible key centers. In a series of two experiments, we investigate meter’s role as an orienting force within these progressions. Both experiments presented listeners with a progression that looped the C major, F major, A minor, and G major triads, a series that could plausibly be heard in multiple keys. While the ordering of the triads remained constant across trials, we modified which chord began the loop, thus altering the placement of the initiating metric accent. In Experiment 1, participants heard the loop followed by a probe chord and were asked to rate the probe’s stability, a proxy for identifying a key center. In Experiment 2, participants saw a notated loop and were asked to select the most stable chord. We found a significant effect of metric position and chord identity, with participants rating metric accents and the C major triad as the most stable/centric event. We use these findings to create an algorithmic key-finding model that incorporates both pitch and metric information. Our study argues that metric position has a strong influence on key perception in popular music, challenging an inherited Western art music bias toward purely pitch-based understandings of musical key.
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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 | 0.000 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".