Noise Suppression Using Gated Recurrent Units and Nearest Neighbor Filtering
Bibliographic record
Abstract
A technique to enhance noisy speech through machine learning and digital signal processing methods is proposed in this paper. In the first step of enhancement, Mel-frequency cepstral coefficients are extracted from the noisy speech and fed to a gated recurrent unit (GRU) network which estimates a sequential gain vector used to improve the signal-to-noise ratio (SNR) of the noisy speech. In the second step of enhancement, nearest neighbor filtering is applied to generate an estimate of the isolated noise spectrogram in the noisy speech. This estimate is used to compute a soft mask which is multiplied with the frequency spectrum of the enhanced noisy speech from the first step. This two-step process achieves good results in SNR conditions of greater than 5 db. Under this threshold, the output speech can be distorted. Noisy speech is artificially generated through two datasets consisting of speech, and noise files to create a training and testing dataset for the GRU network.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".