Identification of weak spots in the sound insulation of walls using a spherical microphone array
Bibliographic record
Abstract
A beamforming microphone array can be useful for the identification of directions of arrival of stronger components of the sound field. When these directions can be traced back to locations in the room containing the array, they can indicate the source of the detected sound. A previously developed spherical array [J. Acoust. Soc. Am. 112, 1980–1991 (2002)] has been used for the identification of localized weak spots in otherwise highly insulating walls. A sound source and the array were placed on opposite sides of a wall sample constructed between two reverberation rooms. The omnidirectional impulse response was measured to each of the 32 array microphones, and subsequent beamforming resulted in 60 directional impulse responses at the array position, distributed over all directions. This set of responses was analyzed to identify directions of peak sound transmission through the wall. The walls tested had STC ratings greater than 50, intentionally modified to contain weak spots or defects, most not severe enough to affect the STC. The results obtained with the array approach were compared to a brute force scan with a microphone located 0.25 m from the wall. The array approach was capable of detecting even minor defects in walls.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".