Variable rate speech and channel coding for mobile communication
Bibliographic record
Abstract
Although the mobile communication channels are time-varying, most systems allocate the combined rate between the speech coder and error correction coder according to a nominal channel condition. This generally leads to a pessimistic design and consequently an inefficient utilization of the available resources, such as bandwidth and power. This paper describes an adaptive coding system that adjusts the rate allocation according to actual channel conditions. Two types of variable rate speech coders are considered : the embedded coders and the multimode coders and both are based on code excited linear prediction (CELP). On the other hand, the variable rate channel coders are based on the rate compatible punctured convolutional codes (RCPC). A channel estimator is used at the receiver to track both the short term and the long term fading condition in the channel. The estimated channel state information is then used to vary the rate allocation between the speech and the channel coder, on a frame by frame basis. This is achieved by sending an appropriate rate adjustment command through a feedback channel. Experimental results show that the objective and the subjective speech quality of the adaptive coders are superior than their non-adaptive counterparts. Improvements of up to 1.35 dB in SEGSNR of the speech signal and up to 0.9 in informal MOS for a combined rate of 12.8 kbit/s have been found. In addition, we found that the multimode coders perform better than their embedded counterparts.>
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".