A Robust Pitch Estimation Approach for Colored Noise-Corrupted Speech
Bibliographic record
Abstract
We present an integrated pitch estimation approach for severely colored noise-corrupted speech. An effective colored noise-whitening process is first applied to the noisy speech. Then, a variable-length average magnitude difference function (VLAMDF) of the pre-filtered noisy speech (PFNS) is proposed, which almost conquers the trend of falling valleys in the conventional AMDF. The amplitude characteristic of the VLAMDF is reshaped by means of a simple linear transformation to reduce the possibility of double-pitch-errors. As the VLAMDF exhibits a valley while the autocorrelation function (ACF) of PFNS provides a peak, the ACF is weighted by the reciprocal of the VLAMDF to emphasize the pitch-candidate as well as to suppress the non-pitch peaks. Moreover, a noise-robust pitch detection in the time-domain is guaranteed by collaboration of this enhanced autocorrelation function with the reshaped version of the VLAMDF. The proposed approach is simulated using the Keele reference database and provides a superior accuracy relative to some of the existing methods implemented in the presence of colored noise, even at a very low signal-to noise ratio (SNR) of -15 dB.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".