Bibliographic record
Abstract
Cochlear implants are being widely used for the patients with severe to profound sensorineural hearing loss.Speech coding algorithms play an important role in improving the performance of cochlear implant.At recent years, the performance of CI has been improved for most users under silent environment.However, as the background noise level increases, speech recognition scores are degraded considerably.In this paper, the Empirical Mode Decomposition and Teager-Kaiser Energy Operator are applied as a speech enhancement method for cochlear implants.This algorithm is developed to extract features, called intrinsic mode functions, by a sifting process.Then, Frequency and amplitude of each IMF is extracted based on TKEO.Finally, performance of this algorithm in terms of correlation analysis was compared to continuous interleaved sampling (CIS), frequency amplitude modulation encoding (FAME) and Hilbert Huang Transform Stimulating (HHTS) strategies.The results showed the highest correlation coefficient between spectrum of synthesized signal and original speech with proposed method.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.939 | 0.920 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".