Air-Borne and Tissue-Borne Sensitivities of Bioacoustic Sensors Used on the Skin Surface
Bibliographic record
Abstract
Measurements of body sounds on the skin surface have been widely used in the medical field and continue to be a topic of current research, ranging from the diagnosis of respiratory and cardiovascular diseases to the monitoring of voice dosimetry. These measurements are typically made using light-weight accelerometers and/or air-coupled microphones attached to the skin. Although normally neglected, air-borne sounds generated by the subject or other sources of background noise can easily corrupt such recordings, which is particularly critical in the recording of voiced sounds on the skin surface. In this study, the sensitivity of commonly used bioacoustic sensors to air-borne sounds was evaluated and compared with their sensitivity to tissue-borne body sounds. To delineate the sensitivity to each pathway, the sensors were first tested in vitro and then on human subjects. The results indicated that, in general, the air-borne sensitivity is sufficiently high to significantly corrupt body sound signals. In addition, the air-borne and tissue-borne sensitivities can be used to discriminate between these components. Although the study is focused on the evaluation of voiced sounds on the skin surface, an extension of the proposed methods to other bioacoustic applications is discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".