Another lesson from Lassus: using computers to analyse counterpoint
Bibliographic record
Abstract
The authors report on experiments they have run using the computer to search a small corpus of Renaissance pieces (the famous Lassus duos of 1577) for recurring contrapuntal combinations. They liken these combinations (or ‘modules’ as Jessie Ann Owens has called them) to words in a text, and the process of finding them, to work done by linguists such as John Sinclair on large corpora of text. The program used was devised by a team at McGill University as part of the ELVIS (‘Electronic Locator of Vertical Interval Successions’) project. The interval successions are identified by the vertical intervals and the melodic motions that connect them, in the manner of Tinctoris’s counterpoint treatise (1477), which illustrates most of the possible ways two vertical intervals can be connected. The authors find that some short interval successions appear, as we would expect, in repetitions of thematic material (i.e. as parts of soggetti associated with specific text phrases). Others, however, occur in apparently run-of-the-mill counterpoint: in the middle of words, in the middle of melismas, across phrase boundaries and embellished in a variety of ways. These often exhibit surprising consistency as to semitone position and possible modal associations.
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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 | 0.004 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.025 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".