Hybrid Transform Based Speech Band Width Enhancement Using Data Hiding
Bibliographic record
Abstract
The limited narrow band (LNB) speech signal spread in the range of 300 to 3400Hz used in public switched telephone networks results in poor-quality telephony speech. Bandwidth extension techniques are performed to expand the frequency range from LNB speech to a clear wideband (CWB) speech signal range of 50Hz-7000Hz over existing public telephone networks. In this paper, a novel robust speech bandwidth extension algorithm by Discrete Wavelet Transform- Discrete Cosine Transform- Based Data Hiding (DWT-DCT-DH) Hybrid transform model was used to spread the out-of-band (3400Hz to 7000Hz) speech frequencies over the LNB speech. In this proposed technique the out-of-band speech frequencies are embedded in LNB speech and imperceptibly spread over the network. These Embedded out-of-band speech frequencies are recovered steadily at the receiver end to generate a restored CWB telephony speech of considerably better quality. The proposed technique simulation results show more intelligible and better-quality telephony speech generated compared to the other bandwidth extension techniques.
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 | 0.000 | 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) | 0.001 | 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".