Lightweight and Optimized Sound Source Localization and Tracking Methods\n for Open and Closed Microphone Array Configurations
Bibliographic record
Abstract
Human-robot interaction in natural settings requires filtering out the\ndifferent sources of sounds from the environment. Such ability usually involves\nthe use of microphone arrays to localize, track and separate sound sources\nonline. Multi-microphone signal processing techniques can improve robustness to\nnoise but the processing cost increases with the number of microphones used,\nlimiting response time and widespread use on different types of mobile robots.\nSince sound source localization methods are the most expensive in terms of\ncomputing resources as they involve scanning a large 3D space, minimizing the\namount of computations required would facilitate their implementation and use\non robots. The robot's shape also brings constraints on the microphone array\ngeometry and configurations. In addition, sound source localization methods\nusually return noisy features that need to be smoothed and filtered by tracking\nthe sound sources. This paper presents a novel sound source localization\nmethod, called SRP-PHAT-HSDA, that scans space with coarse and fine resolution\ngrids to reduce the number of memory lookups. A microphone directivity model is\nused to reduce the number of directions to scan and ignore non significant\npairs of microphones. A configuration method is also introduced to\nautomatically set parameters that are normally empirically tuned according to\nthe shape of the microphone array. For sound source tracking, this paper\npresents a modified 3D Kalman (M3K) method capable of simultaneously tracking\nin 3D the directions of sound sources. Using a 16-microphone array and low cost\nhardware, results show that SRP-PHAT-HSDA and M3K perform at least as well as\nother sound source localization and tracking methods while using up to 4 and 30\ntimes less computing resources respectively.\n
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".