Rhythmic similarity in Flamenco music: Comparing psychological and mathematical measures
Bibliographic record
Abstract
Background in Music Psychology. Previous research on simple synthetic rhythmic patterns revealed the categorical structure of rhythm perception in music [1]. However, further research is needed to investigate complex rhythmic structures. This paper investigates rhythmic similarity in terms of mathematical measures and psychological measures. This study focuses on flamenco music, which is characterized by hand-clapping patterns whose underlying meter is composed of a pattern of soft and accented claps. Background in Computer Science. A mathematical analysis of the five 12/8 rhythmic patterns used in flamenco music was recently conducted to establish several musicological hypotheses [2], and specifically confirmed the existence of an ancestral rhythm for flamenco music. This analysis relied on the similarity between the rhythmic patterns. Such similarity was measured with two different rhythmic similarity measures: the chronotonic distance and the directed swap distance. Aims. Presented in this paper are the results of a listening test conducted to evaluate the perceived similarity of the rhythmic patterns used in flamenco music (5 basic patterns + ancestral rhythm). The psychological ratings of similarity are then compared with the mathematical measures proposed in [2]. The goal of this experiment is compare mathematical measures of rhythmic similarity
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".