Audiovisual correspondence between musical timbre and visual shapes
Bibliographic record
Abstract
This article investigates the cross-modal correspondences between musical timbre and shapes. Previously, such features as pitch, loudness, light intensity, visual size, and color characteristics have mostly been used in studies of audio-visual correspondences. Moreover, in most studies, simple stimuli e.g., simple tones have been utilized. In this experiment, 23 musical sounds varying in fundamental frequency and timbre but fixed in loudness were used. Each sound was presented once against colored shapes and once against grayscale shapes. Subjects had to select the visual equivalent of a given sound i.e., its shape, color (or grayscale) and vertical position. This scenario permitted studying the associations between normalized timbre and visual shapes as well as some of the previous findings for more complex stimuli. One hundred and nineteen subjects (31 females and 88 males) participated in the online experiment. Subjects included 36 claimed professional musicians, 47 claimed amateur musicians, and 36 claimed non-musicians. Thirty-one subjects have also claimed to have synesthesia-like experiences. A strong association between timbre of envelope normalized sounds and visual shapes was observed. Subjects have strongly associated soft timbres with blue, green or light gray rounded shapes, harsh timbres with red, yellow or dark gray sharp angular shapes and timbres having elements of softness and harshness together with a mixture of the two previous shapes. Color or grayscale had no effect on timbre-shape associations. Fundamental frequency was not associated with height, grayscale or color. The significant correspondence between timbre and shape revealed by the present work allows designing substitution systems which might help the blind to perceive shapes through timbre.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".