Speech Bandwidth Extension by Data Hiding and Phonetic Classification
Bibliographic record
Abstract
Speech bandwidth extension can be defined as the deliberate process of expanding the frequency range (bandwidth) for speech transmission. Its significant advancement in recent years has led to the technology being adopted commercially in several areas including psychoacoustic bass enhancement of small loudspeakers and the high frequency enhancement of perceptually coded audio. In this paper, a data hiding method based on dither quantization is used for speech bandwidth extension. More specifically, the out-of-band information is encoded and embedded into the narrowband speech without degrading the quality of the bandlimited signal. At the receiver, when the out-of-band information is extracted from the hidden channel, it can be used to combine with the bandlimited signal, providing a signal with a wider bandwidth. To encode the out-of-band speech more efficiently, acoustic phonetic classification is employed to generate three linear prediction (LP) codebook. The simulation results show that compared with using non-classified codebook, the propose scheme have a better bandwidth extension performance in terms of log spectral distortion (LSD).
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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 | 0.000 | 0.001 |
| 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 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".