Bibliographic record
Abstract
The role of harmonicity in masking was studied by comparing the effect of harmonic and inharmonic maskers on the masked thresholds of noise probes using a three-alternative, forced-choice method. Harmonic maskers were created by selecting sets of partials from a harmonic series with an 88-Hz fundamental and 45 consecutive partials. Inharmonic maskers differed in that the partial frequencies were perturbed to nearby values that were not integer multiples of the fundamental frequency. Average simultaneous-masked thresholds were as much as 10 dB lower with the harmonic masker than with the inharmonic masker, and this difference was unaffected by masker level. It was reduced or eliminated when the harmonic partials were separated by more than 176 Hz, suggesting that the effect is related to the extent to which the harmonics are resolved by auditory filters. The threshold difference was not observed in a forward-masking experiment. Finally, an across-channel mechanism was implicated when the threshold difference was found between a harmonic masker flanked by harmonic bands and a harmonic masker flanked by inharmonic bands. A model developed to explain the observed difference recognizes that an auditory filter output envelope is modulated when the filter passes two or more sinusoids, and that the modulation rate depends on the differences among the input frequencies. For a harmonic masker, the frequency differences of adjacent partials are identical, and all auditory filters have the same dominant modulation rate. For an inharmonic masker, however, the frequency differences are not constant and the envelope modulation rate varies across filters. The model proposes that a lower variability facilitates detection of a probe-induced change in the variability, thus accounting for the masked threshold difference. The model was supported by significantly improved predictions of observed thresholds when the predictor variables included envelope modulation rate variance measured using simulated auditory filters.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".