Improving the detection efficiency of the VMR-WB VAD algorithm on music signals
Bibliographic record
Abstract
Speech codecs are usually equipped with voice activity de-tection (VAD) algorithm to enable efficient coding of inac-tive frames and the discontinuous transmission mode (DTX). High VAD efficiency for speech in noisy environments is often traded off against its robustness for music. This is also the case of the VMR-WB codec recently standardized by 3GPP2. Its VAD fails to detect portions of some critical mu-sic samples. In this contribution we propose a method to improve the performance of the VMR-WB VAD on music signals. The idea is to measure the stability of tones in the spectral domain by means of per-tone correlation analysis. By using this approach, the music detection accuracy is in-creased to ~99 % and the problem of misclassification is significantly reduced. The proposed method has been im-plemented in the G.718 codec being currently standardized by the ITU-T. 1.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".