Immersive auditory awareness: A smart earphones platform for education on noise-induced hearing risks
Bibliographic record
Abstract
The Audio Research Platform developed by the ÉTS-EERS Industrial Research Chair in In-Ear technologies is a digital audio processing wearable device that features a powerful Digital Signal Processor and a pair of wired earplugs featuring outer- and inner-ear microphones as well as miniaturize loudspeaker. ARP has been used since over the last 2 decades by CRITIAS to develop new algorithms for advanced hearing protection and communication in noise, as well as in-ear audio sensing. In this adaptation, the ARP enables transparent hearing of ambient sounds, simulates varying degrees of hearing loss, and includes a tinnitus simulator. ARP also functions as a personal music player, monitoring playback levels and displaying the “Age of your ears”) a metric recently proposed by CRITIAS), predicting accelerated auditory aging from excessive music playback. In the playback mode, wearers experience pre-recorded noise, prompting vocal adjustments known as the “Lombard Effect.” ARP analyzes the wearer's voice using the outer-ear microphone, illustrating level and pitch shifts in speech due to a noisy environment. This paper details ARP's technical aspects, positioning it as a powerful tool to inform the public about noise-induced hearing loss and promote awareness and prevention through enjoyable real-time demonstrations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
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".