An Identification Technique for Noisy ARMA Systems in Correlation Domain
Bibliographic record
Abstract
In this paper, an identification technique for the minimum-phase autoregressive moving average (ARMA) systems using only the noise-corrupted observations is presented. In order to obtain a more accurate estimate of the AR parameters in the noisy environment, a repeated autocorrelation function (RACF) of the observed data is employed in the modified least-squares Yule-Walker equations. It has been found that at a very low signal-to-noise ratio (SNR), the effect of the additive noise can be significantly reduced if a twice-RACF is employed instead of the conventional ACF. Prior to the MA part identification, a noise-compensation scheme is proposed which operates on the noise-contaminated residual signal. The MA parameters are extracted from the noise-compensated power spectrum of the residual signal using the spectral factorization. ARMA systems of different orders and some natural speech signals are tested and computer simulations demonstrate a superior identification results even at a very low SNR.
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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 | 0.003 | 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 | 0.001 |
| 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".