Implementation and evaluation of an accurate real-time voiceband signal classifier
Bibliographic record
Abstract
This paper describes the implementation and resulting accuracy of an economical voiceband signal classifier developed for use in the public switched telephone network. Companded digital signals are extracted from a 1.544 Mbps T1 digital trunk and then classified into either silence or twelve other active categories, including speech, four classes of data modem, three classes of fax, random binary data, fax signalling, ringback signal, and dual tone multifrequency (DTMF) digits. The classifier first derives from the baseband signal in each channel the central second-order moment and the first ten lags of the autocorrelation sequence, all normalized with respect to average power. Avoiding demodulation and Fourier transform steps in the classification process permits linear and quadratic discriminant functions to be computed in real time for all 24 T1 channels. Classification accuracies are reported for statistically optimal linear discriminant functions over classification intervals ranging from 31.5 to 256.5 ms. Also given are the greater accuracies achievable using statistically optimal quadratic discriminant functions and an automatically trained decision tree known as an adaptive logic network (ALN).
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 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) | 0.001 | 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".