Time Difference of Arrival Estimation Based on a Kronecker Product Decomposition
Bibliographic record
Abstract
Time difference of arrival (TDOA) estimation, which often serves as the fundamental step for a source localization or a beamforming system, has a significant practical importance in a wide spectrum of applications. To deal with reverberation, the TDOA estimation problem is often transformed into one of identifying the relative acoustic impulse responses. This letter presents a method to efficiently identify the relative acoustic impulse response between two microphones for TDOA estimation based on the so-called Kronecker product decomposition. By decomposing the relative impulse response into a series of Kronecker products of shorter filters, the original channel identification problem with a long impulse response is converted into one of identifying a number of short filters. Since the TDOA information is embedded only in the direct path of the relative impulse response, the dimension of the Kronecker product decomposition can be very small and, as a result, the developed algorithm is expected to work well in real environments with a small number of data snapshots.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".