Surveying the sounds used in the Journal of the Acoustical Society of America (1950–2017)
Bibliographic record
Abstract
The earliest auditory psychophysical experiments involved naturalistic sounds such as hammers striking plates. The subsequent development and ubiquity of desktop computing gave researchers the ability to more precisely control stimulus parameters such as frequency, amplitude, and duration (Neuhoff, 2004). However much of our everyday listening is for events rather than easily manipulated properties (Gaver, 1993), and the world lacks the kinds of constrained sounds often used in auditory research (Phillips et al., 2002). Although simplistic auditory stimuli hold benefits with respect to control, their disproportionate use poses problems for generalizing outcomes from key experiments. To provide insight into the sounds used in auditory perception research, we surveyed a representative sample of auditory stimuli from 217 psychophysical experiments published in JASA between 1950 and 2017. Our survey documents a disproportionate focus on simplistic sounds, with less than 4% of psychophysical experiments using stimuli exhibiting the dynamic temporal structures characteristic of natural auditory events. We will discuss the implications of these findings in the content of ongoing areas of inquiry of broad relevance to the auditory perception community.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".