Adaptive wavelet packet thresholding with iterative Kalman filter for speech enhancement
Bibliographic record
Abstract
In this paper, we propose an adaptive wavelet packet (WP) thresholding method with iterative Kalman filter (IKF) for speech enhancement. The WP transform is first applied to the noise corrupted speech on a frame-by-frame basis, which decomposes each frame into a number of subbands. For each subband, a voice activity detector (VAD) is designed to detect the voiced/unvoiced parts of the speech. Based on the VAD result, an adaptive thresholding scheme is then utilized to each subband speech to obtain the pre-enhanced speech. To achieve a further level of enhancement, an IKF is next applied to the pre-enhanced speech. The proposed method is evaluated under various noise conditions. Experimental results are provided to demonstrate the effectiveness of the proposed method as compared to some previous works in terms of segmental SNR and perceptual evaluation of speech quality (PESQ) as two well-known performance indexes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".