Bibliographic record
Abstract
Numerous studies have investigated the detection of pure tones and harmonic series in noise, but far fewer studied the perception of noise in harmonic series. One such study [Gockel, Moore and Patterson, J. Acoust. Soc. Am., 111(6), 2759–2770 (2002)] demonstrated that the masking thresholds of noise in harmonic series maskers were affected by the fundamental frequency (F0) and the overall level of the series, and by the relative phase of the harmonic components. The maskers used by Gockel et al. comprised unresolved harmonics (10th and higher harmonics below 5 kHz) for which F0 and frequency range covaried. The current study investigated noise detection for three non-overlapping spectral bands of equal auditory filter bandwidths (ERBs). Bands included either resolved harmonics (B1), unresolved harmonics (B3), or both resolved and unresolved harmonics (B2). Masker F0 and overall level were also varied. A Bayesian linear mixed-effects analysis showed that noise detection was better for higher F0s in B1 and B2, and that a higher presentation level resulted in a small improvement in noise detection in B2. Noise detection was better for higher presentation levels in B3. The findings will be discussed in relation to predictions of auditory processing models [Patterson, Allerhand, Giguère, J. Acoust. Soc. Am. 98(4), 1890–1894 (1995)].
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".