Objective analysis of the effect of memory inclusion on bandwidth extension of narrowband speech
Bibliographic record
Abstract
For the purpose of improving Bandwidth Extension (BWE) of narrowband speech, we continue our recent work on the positive effect of exploiting the temporal correlation of speech on the dependence between speech frequency bands. We have shown that such memory inclusion into MFCC speech parametrization translates into higher highband certainty. In the work presented herein, we employ VQ to estimate highband discrete entropies, thus refining our analysis of the effect of memory inclusion on increasing highband certainty. Moreover, we extend our pre-vious analysis to LSF parameters. We further construct a BWE system that exploits our memory inclusion technique, thus trans-lating highband certainty gains into practical BWE performance improvement as measured by the objective quality of recon-structed speech. Results show that memory inclusion decreases the log-Spectral Distortion of the extended highband speech by as much as 1 dB corresponding to more than 14 % relative.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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 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".