Bibliographic record
Abstract
The capability of the human auditory system is phenomenal. The dynamic range it can handle, the frequency range it covers, and, not the least, its ability to detect and identify speech in the presence of interfering sounds is astonishing. In daily life we use this capability in many ways. We use it for speech communication as well as for alerts and alarms. We use it for analysis of devices and machines, e.g. our computers and cars. Is it on? Does it sound normal, or is something wrong? We also use it for various forms of entertainment. However, there’s a flip side to the great capability. From an engineering point of view it poses challenges when designing buildings, machines, and devices such as phones, computers, headphones, microphones, etc. And although we have a phenomenal ability to understand speech in challenging situations, we often mishear or misunderstand. Human hearing is also quite easily damaged. This paper presents old and new results related to the capability of our hearing, and some of the challenges related to the same. The content of this article was presented at the Acoustics Week in Canada 2014 as one of three invited keynote presentations.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.131 | 0.105 |
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".