A Decomposition-Based Kalman Filter for the Identification of Acoustic Impulse Responses
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Bibliographic record
Abstract
The identification of acoustic impulse responses usually involves long length adaptive filters, with hundreds or even thousands of coefficients. This issue raises significant challenges in terms of both the computational complexity and convergence features. Recently, a decomposition-based solution using the Kronecker product and low-rank approximations was proposed in this context, by exploiting the intrinsic nature of the acoustic impulse responses. These systems are characterized by early reflections and late reverberation, each of these components having different characteristics that should be considered. In this paper, we propose a Kalman filter following this approach, which outperforms the previously developed solution based on the recursive least-squares algorithm. Simulations performed in the framework of acoustic echo cancellation support the performance features 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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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