New Sub-Band Proportionate Variable NLMS Algorithm for the Identification of Acoustical Dispersive-and-Sparse Impulse Responses
Bibliographic record
Abstract
This paper presents a modified algorithm for addressing acoustic impulse responses identification for communication systems.This study introduces an enhanced sub-band version of variable-step-size -law proportionate normalized least-mean-square algorithm for achieving rapid convergence and minimal steady-state error.This algorithm is noted as SPV-NLMS: Sub-band Proportionate Variables-step-sizes NLMS algorithm.The proposed SPV-NLMS algorithm is dynamically and independently adjusting the N step-sizes parameters during adaptation using an optimal estimation of each sub-filter.The SPV-NLMS is adaptable and can be employed with various acoustic more dispersive, dispersive, more sparse or sparse environments.The effectiveness of this algorithm is validated through simulations in the context of acoustic impulse response identification.The SPV-NLMS algorithm has the potential to significantly improve the convergence and the steady-state error using the Mean Square Error (MSE) and Echo Return Loss Enhancement (ERLE) criteria.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".