Bibliographic record
Abstract
A new approach to sound localization, known as enhanced sound localization, is introduced, offering two major benefits over state-of-the-art algorithms. First, higher localization accuracy can be achieved compared to existing methods. Second, an estimate of the source orientation is obtained jointly, as a consequence of the proposed sound localization technique. The orientation estimates and improved localizations are a result of explicitly modeling the various factors that affect a microphone's level of access to different spatial positions and orientations in an acoustic environment. Three primary factors are accounted for, namely the source directivity, microphone directivity, and source-microphone distances. Using this model of the acoustic environment, several different enhanced sound localization algorithms are derived. Experiments are carried out in a real environment whose reverberation time is 0.1 seconds, with the average microphone SNR ranging between 10-20 dB. Using a 24-element microphone array, a weighted version of the SRP-PHAT algorithm is found to give an average localization error of 13.7 cm with 3.7% anomalies, compared to 14.7 cm and 7.8% anomalies with the standard SRP-PHAT technique.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".