Multiple moving speaker tracking by microphone array on mobile robot
Why this work is in the frame
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Bibliographic record
Abstract
Real-world applications often require tracking multiple moving speakers for improving human-robot interactions and/or sound source separation. This paper presents multiple moving speaker tracking using an 8ch microphone array system installed on a mobile robot. This problem is difficult because the system does not assume that sound sources and/or the microphone array are fixed. Our solutions consist of two key ideas – time delay of arrival estimation, and multiple Kalman filters. The former localizes multiple sound sources based on beamforming in real time. Non-linear movements are tracked by using a set of Kalman filters with different history lengths in order to reduce errors in tracking multiple moving speakers under noisy and echoic environments. For quantitative evaluation of the tracking, motion references of sound sources and a mobile robot, called SIG2, were measured accurately by ultrasonic 3D tag sensors. As a result, we showed that the system tracked three simultaneous sound sources even when SIG2 moved in a room with large reverberation due to glass walls. 1.
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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 | 0.000 | 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 | 0.001 | 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