Voice Activity Detection Based on Wavelet Packet Transform in Communication Nonlinear Channel
Bibliographic record
Abstract
This paper presents a voice activity detection (VAD) algorithm based on the Wavelet Packet Transform and the Teager Energy Operation (TEO) processing. The signal is decomposed into subband signals. We used the multi-resolution analysis property of the Wavelet Transform to extract and analyse time-frequency components corresponding to speech. In order to obtain a parameter called Voice Activity Shape (VAS), we used TEO processing to better distinguish subband signals corresponding to speech. The subband variance values of each TEO signal are summed to obtain the VAS, which is higher in speech regions than in non speech regions. Experimental results show that our VAD perform better than the G729B, particularly in difficult noise conditions and also in the case when the speech sound is passed in a nonlinear communication channel. Experimental results are shown in the case of real speech communications from a spaceship to terrestrial 3G cellular network assuming nonlinear interferences.
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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".