Acoustic source tracking in a reverberant environment using a pairwise synchronous microphone network
Bibliographic record
Abstract
Abstract—This paper considers acoustic source tracking in a room environment using a distributed microphone pair network. Existing time-delay of arrival (TDOA) based approaches usually require all received signals to be transmitted to central processor and synchronized to extract the TDOA measurements. The source positions are then obtained by using a subsequent localization or tracking approach. In this paper, we propose a distributed particle filtering (PF) approach to track the source using a microphone pair network. Each node is constructed by a microphone pair and TDOA measurements are extracted at local nodes. An extended Kalman filter based PF is developed to estimate the first order and the second order statistics of the source state. A consensus filter is then applied to fuse these local statistics between neighboring nodes to achieve a global estimation. Under such an approach, only the state statistics need to be transmitted and the received signals need only to be pairwise synchronized. Consequently, both communication and computational cost can be significantly reduced. Simulations under different reverberant environments demonstrate that the proposed approach outperforms the centralized sequential importance sampling based PF approach in single source tracking as well as in nonconcurrent multiple source tracking. Index Terms—Acoustic source tracking, reverberant environment, time-delay of arrival, extended Kalman particle filtering, consensus filter. I.
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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".