Finding a Fingerprint. Microtiming and Tempo Variability in Five Renowned Rock Drummers
Bibliographic record
Abstract
In this article, we analyze microtiming and tempo variability in the music of five renowned rock drummers: Ringo Starr, Mitch Mitchell, Stewart Copeland, Phil Rudd, and Meg White. Using the applications Moises and Sonic Visualiser, we isolated the drum stems of 79 studio and live recordings and determined exact quarter-note positions in backbeat patterns. We used metrics such as the normalized absolute deviation beat sum, average beat coefficient of variation, and tempo coefficient of variation in order to capture these drummers’ distinctive “fingerprints.” We found that Starr showed a propensity for delayed backbeats and slowing down, Copeland a tendency for early backbeats, Rudd steadiness without a click track, and Mitchell and White a propensity towards wide variability in timing and tempo. The results of this exploratory study suggest that drumming characteristics are shaped not only by personal style but also by broader historical trends.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".