Construction of discriminative positive time-frequency distributions
Bibliographic record
Abstract
Positive time-frequency energy distributions (PTFD) are suitable for studying the non-stationary dynamics of a signal. Instantaneous features extracted from the PTFD are often used in classification applications where the discriminatory clue lies in the non-stationary behavior of the signal. From a classification point of view it would be desirable to identify and extract instantaneous features that correspond to only the discriminative portion of the signal. By doing so we get an added advantage of eliminating the overlap from the non-discriminatory portion of the signal in the instantaneous feature extraction process. In this paper, we propose a front-end processing using a novel time-width versus frequency band mapping that facilitates the construction of PTFD corresponding to only the discriminatory portion of the signal. Instantaneous features extracted from these PTFDs are readily discriminative and attractive for classification and characterization applications. The proposed method is demonstrated with a speech classification example.
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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".