Auditory Streaming Complexity and Renaissance Mass Cycles
Bibliographic record
Abstract
How did Renaissance listeners experience the polyphonic mass ordinary cycle in the soundscape of the church? We hypothesize that the textural differences in complexity between mass movements allowed listeners to track the progress of the service, regardless of intelligibility of the text or sophisticated musical knowledge. Building on the principles of auditory scene analysis, this article introduces the Auditory Streaming Complexity Estimate, a measure to evaluate the blending or separation of each part in polyphony, resulting in a moment-by-moment tally of how many independent streams or sound objects might be heard. When applied to symbolic scores for a corpus of 216 polyphonic mass ordinary cycles composed between c. 1450 and 1600, we show that the Streaming Complexity Estimate captures information distinct from the number of parts in the score or the distribution of voices active through the piece. While composers did not all follow the same relative complexity strategy for mass ordinary movements, there is a robust hierarchy emergent from the corpus as a whole: a shallow V shape with the Credo as the least complex and the Agnus Dei as the most. The streaming complexity of masses also significantly increased over the years represented in this corpus.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".