Beam shape, focus index, and localization error for performance evaluation of a multisensor stethoscope beamformer
Bibliographic record
Abstract
This paper proposes methods for evaluating a multisensor system consisting of several stethoscope chest pieces simultaneously recording and storing audio data for computer-aided analysis. To illustrate this concept, we use a circular array of microphones in a free-field homogeneous medium and use delay-and-sum beamforming to combine the received signals. The evaluation methods shown here are applicable to any other non-homogeneous medium and beamforming technique. Beam shape, focus index, and localization error are the methods of performance evaluation discussed in this paper. Several beam shapes are shown and described. The proposed "focus index", a new performance metric, compares the power of signals received from different areas within the array and comes up with a numerical indicator of the focus achieved by the beamformer. This paper also describes a method for localizing a sound source in the near-field of a microphone array by comparing the delay between signals received at known microphone locations. Simulation as well as verification results are presented.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".