The unknown effects of amplitude envelope: A survey of <i>Hearing Research</i>
Bibliographic record
Abstract
In auditory research, the use of amplitude-steady tones with abrupt onsets and offsets is quite common. While these types of “flat” tones offer a great deal of control, they are not representative of the types of sounds we hear outside the laboratory. In everyday listening we are much more likely to encounter “percussive” (i.e., exponentially decaying) sounds, with offsets conveying detailed information such as the materials and force used to produce the sounds—information that is absent in abruptly ending flat tones. Given that differences in perception have been reported when using different amplitude envelopes (Grassi and Pavan, 2012; Neuhoff, 1998; Schutz, 2009), we became interested in determining the prevalence of flat and percussive tones in auditory research publications. Here, we surveyed the journal Hearing Research and classified the temporal structure of sounds used into five categories: flat, percussive, click train, other, and undefined. We found 42.5% of sounds were flat (approximately 13% were click trains, 4% other, and 40% undefined). This finding is consistent with our previous surveys of Music Perception and Attention, Perception, and Psychophysics, suggesting that flat tones dominate auditory research, and the perceptual effects of more naturalistic sounds are relatively unknown and ripe for future exploration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".