Identification of Autoregressive Systems in Noise Based on a Ramp-Cepstrum Model
Bibliographic record
Abstract
In this paper, a new approach for the identification of a minimum-phase autoregressive (AR) system in the presence of a heavy noise is presented. First, a model, valid for both white noise and periodic impulse-train excitations, for the ramp-cepstrum (RC) of the one-sided autocorrelation function of an AR signal is proposed. A residue-based least-squares optimization technique is then employed in conjunction with the RC model to estimate the AR parameters from a noisy output, with a guaranteed system stability. The proposed ramp-cepstral model fitting combines the good features of both the correlation and cepstral domains, and thus provides a more accurate estimate of the parameters in a noisy environment. Extensive simulations are carried out on synthetic AR systems of different orders in the presence of white as well as colored noise. Simulation results demonstrate quite a satisfactory identification performance even for a signal-to-noise ratio of -5 dB, a level at which most of the existing methods fail to provide accurate estimation. To illustrate the suitability of the proposed technique in practical applications, a spectral estimation of a human vocal-tract system is carried out using noise-corrupted natural speech signals.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".