Towards an Acoustic-Semantic Space of Extreme Metal Vocal Styles
Bibliographic record
Abstract
Extreme vocal techniques typical for metal, e.g. growling or screaming, are characterized by low harmonicity and high roughness and associated with expressive dimensions like "aggressiveness" [Tsai et al., 2010]. Based on audio features, classification into broad style categories is possible [Kalbag & Lerch, 2022]. Which audio features are associated with the perception of emerging expressive techniques/stylistic devices that go beyond previously known categories remains open. Short phrases were extracted from 105 metal vocal tracks, 10 pilot-rated by subjects for pairwise similarity (45 comparisons). The resulting similarity matrix serves as basis for a perceptual similarity space computed using multidimensional scaling (MDS). In another pilot experiment, free verbal associations are collected for all 105 excerpts. Preliminary analyses reveal a three-dimensional similarity space whose first major axis represents the contrast between harmonic vs. more inharmonic/rough singing (Harmonic-to-Noise Ratio: r=0.837, p=0.005; Spectral Complexity: r=-0.959, p
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".