A Robust Pitch Estimation Algorithm in Noise
Bibliographic record
Abstract
In this paper, we present a robust pitch estimation algorithm for noise-degraded speech. We propose a new circular average magnitude sum function (CAMSF) and a pseudo normalized correlation function (PNCF) both of which exhibit the periodicity at the pitch period of voiced speech. Exploiting the fact that CAMSF produces a peak while PNCF shows a notch, an integrated time-domain function (ITDF) is developed to enhance the pitch-harmonic-notches in presence of noise. Moreover, a frequency-frame relative smoothed noisy spectrum that acts as a harmonic spectral structure enhancer is utilized to accurately acquire a pitch-harmonic (PH) from noisy speech. We argued that employing the PH, pitch information can be effectively extracted through a variable-period impulse-train in conjunction with the proposed ITDF. It has been ascertained that the overall algorithm simulated using the Keele reference database is able to outperform some of the existing methods and well suited for a wide range of signal-to-noise ratios (SNRs) upto-10 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 | 0.000 |
| 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 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".