A new segmentation technique for noisy multi-component signals using wavelet transform
Bibliographic record
Abstract
A new segmentation method of multi-component noisy signals using wavelet transform is proposed, when the signal components are closely spaced and the time interval between adjacent signal components are unknown. It is shown that Morlet wavelet transform is useful for segmenting a noisy signal, when the signal components are closely spaced. The segmentation problem is formulated using the paradigm of estimating the locations and durations of noisy narrow gaps of the input noisy signals. A wavelet scale sequence comprising of the highest absolute scales for each time instant is employed as test statistics for segmentation. A number of selected local maxima obtained from the wavelet scale sequence correspond to the position of the noisy gaps. Finally, windowed approximate entropy is calculated for the masked noisy signal to estimate the locations and durations of the narrow noisy gap as well as the noisy segments. The proposed scheme is evaluated on simulated examples.
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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 | 0.001 | 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 | 0.001 |
| 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".