"IT LOOKS LIKE SOUND!" : DRAWING A HISTORY OF "ANIMATED MUSIC" IN THE EARLY TWENTIETH CENTURY
Bibliographic record
Abstract
In the early 1930s, film sound technicians created completely synthetic sound by drawing or photographing patterns on the soundtrack area of the filmstrip. Several artists in Germany, Russia, England, and Canada used this innovation to write what came to be called "animated music" or "ornamental sound." It was featured in a few commercial and small artistic productions and was enthusiastically received by the public. It was heralded as the future of musical composition that could eliminate performers, scores, and abstract notation by one system of graphic sound notation and mechanized playback. Its popularity among mainstream filmmaking did not last long, however, due to its limited development. The artists drawing animated sound were dependent entirely upon their technological medium, and when the sound-on-film system faded from popularity and production, so did their art. By examining from a musicological perspective, for the first time, specific examples of animated music from the work of Norman McLaren, Oskar Fischinger, Rudolph Pfenninger, and several filmmakers in Russia, this thesis enumerates the techniques used in animated sound. It also explores the process of its creation, adaptation, and decline. In doing so, it reveals an important chapter in the little-known early history of modern synthesized sound alongside the futuristic musical ideas it both answered and inspired.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".