Approximating and exploiting the residual redundancies-applications to efficient reconstruction of speech over noisy channels
Bibliographic record
Abstract
Exploiting the residual redundancy in a source coder output stream during the decoding process has been proven to be a bandwidth efficient way to combat the noisy channel degradations. We consider soft reconstruction of LSF parameters in the IS-641 CELP coder transmitted over a noisy channel. We propose two schemes. The first scheme attempts to exploit the interframe residual redundancies in the sequence of received parameters. The second approach exploits both interframe and intraframe residual redundancies. Simulation results are provided which demonstrates the efficiency of the algorithms. Another issue addressed here, is a methodology to efficiently approximate and store the residual redundancies or the a priori transition probabilities. For quantizers with high rates calculating these probabilities require a huge number of source samples, and storing them also require a large amount of memory. These issues can well make the decoder design process an impractical task. The proposed method is based on the classification of the signal domain. The presented schemes provide high quality error concealment solutions for CELP coders.
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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 | 0.002 |
| 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".