Multiple Description Conjugate Vector Quantizers with Side Distortion Compensation
Bibliographic record
Abstract
Conjugate vector quantizer (CVQ), a joint source channel coding scheme robust to channel bit errors, is used in various popular speech coders such as ITU-T G.729 and ISO/IEC MPEG4 audio. We propose two multiple description CVQ (MD-CVQ) schemes for combating channel erasure errors. MD-CVQ offers an advantage of moderate computational complexity and storage over conventional MD vector quantizers (MDVQs). Experiments are performed for both i.i.d. Gaussian source and speech/audio signals. Results show that for low channel loss rates, a tradeoff exists between choosing MD-CVQ for its low complexity and MDVQ for its higher signal-to-noise ratio (SNR) performance. For medium to high channel loss rates, MD-CVQ is preferred for its low complexity and comparable SNR performance to MDVQ
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".