Expressing Tonal Closure in Music Performance: Auditory and Visual Cues
Bibliographic record
Abstract
We examined whether musical performers communicate tonal closure through expressive manipulation of facial expressions and non-pitch features of the acoustic output. Two musicians hummed two versions of Silent Night: one ended on the tonic of the scale and exhibited tonal closure; the other ended on the dominant and was therefore tonally unclosed. In Experiment 1, video-only recordings of the hummed sequences were presented to 15 participants, who judged whether the (imagined) melody was closed or unclosed. Accuracy was reliably above chance, indicating that the musicians expressed tonal closure in facial expressions and listeners decoded these cues. Experiment 2 was conducted to determine whether musicians also communicate tonal closure in acoustic attributes other than pitch. All tones in the hummed melodies were pitched-shifted to a constant mean value, but performances still differed in loudness, microtonal pitch variation, timing, and timbre. Participants judged whether audio-only recordings were closed or unclosed. Accuracy was not above chance overall, but was marginally above chance for judgement of one of the two singers. Results suggest that tonal closure can be mapped onto non-pitch aspects of performance expression, but is primarily restricted to the use of facial expressions.
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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) | 0.001 | 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".