Speech enhancement using adaptive neuro-fuzzy filtering
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents an adaptive neuro-fuzzy filtering scheme using the artificial neuro-fuzzy inference system (ANFIS) for noise reduction in speech. The measurable output noisy speech with 5 dB SNR level is taken as the contaminated version of the interference to compare with the output data of the filter. The white noise source is taken as the input. With separate sets of input and output vectors formed after subtractive cluster estimation, an initial first-order (Takagi-Sugeno-Kang) TSK fuzzy inference system (FIS) is generated. The number of rules and antecedent membership functions of the FIS is determined based on the estimated cluster centres and then uses linear least squares estimation to determine each rule's consequent equations. This function returns the initial FIS structure that contains a set of fuzzy rules to cover the feature space. Finally, the ANFIS hybrid-learning algorithm that combines the recursive least-squares estimation (RLSE) method and the back propagation gradient descent (BP/GD) is applied to determine the premise and the consequent parameters. After training, the ANFIS output (i.e. estimated interference) was determined. Then the estimated information signal is calculated as the difference between the measured signal and the estimated interference. It was noted that without extensive training, the ANFIS could do a fairly good job in adaptive denoising of a speech system with nonlinear characteristics.
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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 it