Similar abilities of musicians and non-musicians to segregate voices by fundamental frequency
Bibliographic record
Abstract
Musicians can sometimes achieve better speech recognition in noisy backgrounds than non-musicians, a phenomenon referred to as the "musician advantage effect." In addition, musicians are known to possess a finer sense of pitch than non-musicians. The present study examined the hypothesis that the latter fact could explain the former. Four experiments measured speech reception threshold for a target voice against speech or non-speech maskers. Although differences in fundamental frequency (ΔF0s) were shown to be beneficial even when presented to opposite ears (experiment 1), the authors' attempt to maximize their use by directing the listener's attention to the target F0 led to unexpected impairments (experiment 2) and the authors' attempt to hinder their use by generating uncertainty about the competing F0s led to practically negligible effects (experiments 3 and 4). The benefits drawn from ΔF0s showed surprisingly little malleability for a cue that can be used in the complete absence of energetic masking. In half of the experiments, musicians obtained better thresholds than non-musicians, particularly in speech-on-speech conditions, but they did not reliably obtain larger ΔF0 benefits. Thus, the data do not support the hypothesis that the musician advantage effect is based on greater ability to exploit ΔF0s.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".