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 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 | 0.001 | 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 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".