Bibliographic record
Abstract
Digital filters convolve a signal with an impulse response. This is equivalent to computing a weighted sum of delayed versions of the signal, with the signal versions indexed by their sample-delay and the weights given by the filter impulse response. This talk explores a different approach to filtering. Rather than computing a weighted sum of data values, the stochastic filter uses a sequence of random indices to select output data values from delayed versions of the signal. No summation is done. The probability distribution of the random indices is made identical to the normalized modulus of the impulse response of the corresponding conventional filter. For indices with negative weights, data values are negated. For a random index sequence s(n) the stochastic filter output is simply y(n)=±x(n−s(n)). The output of such a stochastic filter is noisy but it is equal in the mean to the output of the corresponding conventional filter. If the sequence of random indices is identically and independently distributed then the noise is spectrally white. Such a filter can be useful for estimating spectral parameters with minimal computation. The principle is illustrated by an application to double-talk detection for telephones.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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 source (direct Gemma or distilled Codex), 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".