Speech Bandwidth Extension by Data Hiding and Phonetic Classification
Why this work is in the frame
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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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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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it