Musical Melody and Speech Intonation: Singing a Different Tune
Bibliographic record
Abstract
Music and speech are often cited as characteristically human forms of communication. Both share the features of hierarchical structure, complex sound systems, and sensorimotor sequencing demands, and both are used to convey and influence emotions, among other functions [1]. Both music and speech also prominently use acoustical frequency modulations, perceived as variations in pitch, as part of their communicative repertoire. Given these similarities, and the fact that pitch perception and production involve the same peripheral transduction system (cochlea) and the same production mechanism (vocal tract), it might be natural to assume that pitch processing in speech and music would also depend on the same underlying cognitive and neural mechanisms. In this essay we argue that the processing of pitch information differs significantly for speech and music; specifically, we suggest that there are two pitch-related processing systems, one for more coarse-grained, approximate analysis and one for more fine-grained accurate representation, and that the latter is unique to music. More broadly, this dissociation offers clues about the interface between sensory and motor systems, and highlights the idea that multiple processing streams are a ubiquitous feature of neuro-cognitive architectures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 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".