Bibliographic record
Abstract
Recently, with the advances in digital signal processing, compression of biomedical signals has received great attention for telemedicine applications. In this paper, an adaptive transform coding-based method for compression of respiratory and swallowing sounds is proposed. Using special characteristics of respiratory sounds, the recorded signals are divided into stationary and nonstationary portions, and two different bit allocation methods (BAMs) are designed for each portion. The method was applied to the data of 12 subjects and its performance in terms of overall signal-to-noise ratio (SNR) values was calculated at different bit rates. The performance of different quantizers was also considered and the sensitivity of the quantizers to initial conditions has been alleviated. In addition, the fuzzy clustering method was examined for classifying the signal into different numbers of clusters and investigating the performance of the adaptive BAM with increasing the number of classes. Furthermore, the effects of assigning different numbers of bits for encoding stationary and nonstationary portions of the signal were studied. The adaptive BAM with variable number of bits was found to improve the SNR values of the fixed BAM by 5 dB. Last, the possibility of removing the training part for finding the parameters of adaptive BAMs for each individual was investigated. The results indicate that it is possible to use a predefined set of BAMs for all subjects and remove the training part completely. Moreover, the method is fast enough to be implemented for real-time application.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".