A wideband speech and audio codec at 16/24/32 kbit/s using hybrid ACELP/TCX techniques
Bibliographic record
Abstract
A hybrid ACELP/TCX algorithm for coding speech and music signals at 16, 24, and 32 kbit/s is presented. The algorithm switches between algebraic code excited linear prediction (ACELP) and transform coded excitation (TCX) modes on a 20-ms frame basis. Applying TCX on 20 ms frames improved the quality for music signals. Special care was taken to alleviate the switching artifacts between the two modes resulting in a transparent switching process. Subjective test results showed that for speech signals, the performance at 16, 24, and 32 kbit/s, is equivalent to G.722 at 48, 56, and 64 kbit/s, respectively. For music signals, the quality at 24 kbit/s was found equivalent to G.722 at 56 kbit/s. However, at 16 kbit/s, the quality for music was slightly lower than G.722 at 48 kbit/s.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".