What is a melody? On the relationship between pitch and brightness of timbre
Bibliographic record
Abstract
Previous studies showed that the perceptual processing of sound sequences is more efficient when the sounds vary in pitch than when they vary in loudness. We show here that sequences of sounds varying in brightness of timbre are processed with the same efficiency as pitch sequences. The sounds used consisted of two simultaneous pure tones one octave apart, and the listeners' task was to make same/different judgments on pairs of sequences varying in length (one, two, or four sounds). In one condition, brightness of timbre was varied within the sequences by changing the relative level of the two pure tones. In other conditions, pitch was varied by changing fundamental frequency, or loudness was varied by changing the overall level. In all conditions, only two possible sounds could be used in a given sequence, and these two sounds were equally discriminable. When sequence length increased from one to four, discrimination performance decreased substantially for loudness sequences, but to a smaller extent for brightness sequences and pitch sequences. In the latter two conditions, sequence length had a similar effect on performance. These results suggest that the processes dedicated to pitch and brightness analysis, when probed with a sequence-discrimination task, share unexpected similarities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".