Semantic Dimensions of Sound Mass Music
Bibliographic record
Abstract
We combine perceptual research and acoustic analysis to probe the messy, pluralistic world of musical semantics, focusing on sound mass music. Composers and scholars describe sound mass with many semantic associations. We designed an experiment to evaluate to what extent these associations are experienced by other listeners. Thirty-eight participants heard 40 excerpts of sound mass music and related contemporary genres and rated them along batteries of semantic scales. Participants also described their rating strategies for some categories. A combination of qualitative stimulus analyses, Cronbach’s alpha tests, and principal component analyses suggest that cross-domain mappings between semantic categories and musical properties are statistically coherent between participants, implying non-arbitrary relations. Some aspects of participants’ descriptions of their rating strategies appear to be reflected in their numerical ratings. We sought quantitative bases for these associations in the acoustic signals. After attempts to correlate semantic ratings with classical audio descriptors failed, we pursued a neuromimetic representation called spectrotemporal modulations (STMs), which explains much more of the variance in semantic ratings. This result suggests that semantic interpretations of music may involve qualities or attributes that are objectively present in the music, since computer simulation can use sound signals to partially reconstruct human semantic ratings.
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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 | 0.002 | 0.010 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| 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".