The Fusion of Distributed Microphone Arrays for Sound Localization
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Bibliographic record
Abstract
This paper presents a general method for the integration of distributed microphone arrays for localization of a sound source. The recently proposed sound localization technique, known as SRP-PHAT, is shown to be a special case of the more general microphone array integration mechanism presented here. The proposed technique utilizes spatial likelihood functions (SLFs) produced by each microphone array and integrates them using a weighted addition of the individual SLFs. This integration strategy accounts for the different levels of access that a microphone array has to different spatial positions, resulting in an intelligent integration strategy that weighs the results of reliable microphone arrays more significantly. Experimental results using 10 2-element microphone arrays show a reduction in the sound localization error from 0.9 m to 0.08 m at a signal-to-noise ratio of 0 dB. The proposed technique also has the advantage of being applicable to multimodal sensor networks.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it