Sparse-representation algorithms for blind estimation of acoustic-multipath channels
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Bibliographic record
Abstract
Acoustic channel estimation is an important problem in various applications. Unlike many existing channel estimation techniques that need known probe or training signals, this paper develops a blind multipath channel identification algorithm. The proposed approach is based on the single-input multiple-output model and exploits the sparse multichannel structure. Three sparse representation algorithms, namely, matching pursuit, orthogonal matching pursuit, and basis pursuit, are applied to the blind sparse identification problem. Compared with the classical least squares approach to blind multichannel estimation, the proposed scheme does not require that the channel order be exactly determined and it is robust to channel order selection. Moreover, the ill-conditioning induced by the large delay spread is overcome by the sparse constraint. Simulation results for deconvolution of both underwater and room acoustic channels confirm the effectiveness of the proposed approach.
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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 | 0.001 | 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 | 0.001 | 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