Acoustic imaging using a 64-node microphone array and beamformer system
Bibliographic record
Abstract
Acoustic imaging is difficult to achieve in noisy and reverberant environments. Microphone arrays offer an effective approach to obtaining a clean recording of desired acoustic signals in these environments. In this paper, we have designed, implemented, and evaluated a 64-node microphone array system for acoustic imaging. We have applied a delay-and-sum beamforming algorithm for sound source amplification in a noisy environment, and have explored the uses of the array and beamformer by generating the sound intensity map to reconstruct the acoustic scene of interest. Our experimental results show a mean error of 1.1 degrees for sound source localization, and a mean error of 13.1 degrees for source separation. In addition, we also used the system to image seven different materials with audible sound, and obtained their reconstructed acoustic maps as well as frequency response curves, from which we are able to detect the differences between textures based on their acoustic response powers.
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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 | 0.001 |
| 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".