AM-FM analysis of a chirp multicomponent signal employing MWL criterion
Bibliographic record
Abstract
A low complexity algorithm is proposed to decompose a signal into chirp components. The input signal is modeled as a summation of amplitude modulated-frequency modulated (AM-FM) components, where the number of components is known. The algorithm estimates each component individually while considers a second order innovation model for the phase of each component. Windowed likelihood function (WLF) is used as the cost function, as it weights samples differently and can be utilized to suit signal characteristics such as bandwidth. The estimation process is achieved in two steps. First assuming the frequencies are known the amplitudes are estimated by a closed-form solution. In the second step, the frequencies and the frequency change rates are adaptively tracked based on the estimated amplitudes. Lock-in-range of the algorithm is reported for different windows. Simulations are conducted to study the performance of the algorithm to decompose a signal with known components in different circumstances as well as a voiced segment of speech signal
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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 | 0.001 |
| 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".