Bibliographic record
Abstract
Professional musicians have to deal with two great problems when wearing hearing protection devices (HPDs): the occlusion effect and the isolation effect. The occlusion effect is an unnatural and annoying perception of one’s own voice when wearing HPDs. It will affect all musicians whose instrument induces vibrations to the skull, including a singer's vocal tracts and instruments mechanically coupled to the head, such as a trumpet or a violin. The isolation effect is the unnatural sensation of being isolated from a given sound environment. It is caused by a non-uniform attenuation of the HPD over the frequency spectrum and the absence of compensation for psychoacoustic factors, such as uneven loudness perception. These two effects cause a shift of perception between the musician's perception and the audience's perception and therefore compromise the musician’s ability to offer a good performance to his audience. This paper presents the design and implementation of an active musician’s HPD featuring a feedback active noise control system for occlusion effect reduction as well as psychoacoustic compensations for isolation effect reduction. The proposed test procedure and preliminary performance assessments are presented to validate both the test procedure and the system for future subjects trials on a larger scale.
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 | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".