Absolute pitch judgments of familiar melodies generalize across timbre and octave
Bibliographic record
Abstract
Most listeners can determine when a familiar recording of music has been shifted in musical key by as little as one semitone (e.g., from B to C major). However, it is unclear how this form of pitch memory relates to absolute pitch (AP) representations, which are based on pitch chroma. This is because listeners could use spectral cues from the familiar instrumentation of the original recordings or strategies based on pitch height (e.g., relying on a feeling that an incorrect recording sounds “too high” or “too low”) to determine when a familiar recording has been shifted in pitch. Neither of these strategies would require the kind of understanding of pitch chroma or musical key associated with AP. The present experiments thus assessed whether listeners could make accurate absolute pitch judgments when listening to novel renditions of these familiar melodies, differing from the iconic recording in complexity and timbre (Experiment 1) or timbre and octave (Experiment 2). These experiments eliminate the possibility of reliance on spectral cues and pitch height, respectively. Listeners in both experiments selected the correct-key version of the familiar melody at rates that were significantly above chance. These results fit within a growing body of research supporting the idea that most listeners, regardless of formal musical training, have robust representations of absolute pitch - based on pitch chroma - that generalize to novel listening situations. Implications for theories of auditory pitch memory are discussed.
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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 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".