Speech enhancement using adaptive neuro-fuzzy filtering
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.
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 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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".