Amplitude modulation perceptually distinguishes music and speech
Bibliographic record
Abstract
Music and speech are complex and distinct auditory signals that are both foundational to the human experience. The mechanisms underpinning each domain are widely investigated. However, how little acoustic information is in fact required to distinguish between them remains an open question. Here we test the hypothesis that a sound’s amplitude modulation (AM) is a critical acoustic feature. In contrast to paradigms using ecologically valid, complex acoustic signals (that can be challenging to interpret), we use an aggressively reductionist approach: if AM rate and AM regularity are critical for perceptually distinguishing music and speech, the judgement on artificially noise-synthesized ambiguous audio signals should align with their AM parameters. Across four experiments (N = 335), signals with a higher peak AM frequency tend to be judged as speech and lower AM as music, especially among musically sophisticated listeners. In addition, noise signals with more regular AM are judged as music. The data suggest that the auditory system can rely on a low-level acoustic property as basic as AM to distinguish music from speech, a surprising principle that provokes both neurophysiological and evolutionary experiments and speculations.
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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 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".