Improved Recovery of Compressive Sensed Speech
Bibliographic record
Abstract
Compressive Sensing (CS) was proposed as a promising compression technique at the sensing stage in a sensor for reducing size, cost and power of the sensor system. Recently a segmented approach was introduced at the sensing stage of CS and Kronecker-based technique was proposed for improving the recovery. This paper applies CS on windowed speech signals with 50% overlap and augments the performance of the Kronecker-based CS recovery technique by combining the overlapped part of the recovered windowed segments to obtain the recovered speech signal. Windowing of the speech signal reduces the spectral leakage while overlap reclaims the lost power due to windowing. The proposed improved method is tested on 8 female and 8 male speech signals from the TIMIT database. The proposed method achieved up to 14dB SNR improvement while recovering compressively sensed female and male speech over Standard CS recovery technique without overlap with a compression factor (CF) of 10%. The improvement reduced to 5dB at 50% CF.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 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".