Robust blind identification of room acoustic channels in symmetric alpha-stable distributed noise environments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Blind multichannel identification is generally sensitive to background noise. Although there have been some efforts in the literature devoted to improving the robustness of blind multichannel identification with respect to noise, most of those works assume that the noise is Gaussian distributed, which is often not valid in real room acoustic environments. This paper deals with the more practical scenario where the noise is not Gaussian. To improve the robustness of blind multichannel identification to non-Gaussian noise, a robust normalized multichannel frequency-domain least-mean M-estimate algorithm is developed. Unlike the traditional approaches that use the squared error as the cost function, the proposed algorithm uses an M-estimator to form the cost function, which is shown to be immune to non-Gaussian noise with a symmetric α-stable distribution. Experiments based on the identification of a single-input/multiple-output acoustic system demonstrate the robustness of the proposed algorithm.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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