A pitch extraction algorithm in noise based on temporal and spectral representations
Bibliographic record
Abstract
In this paper, a new algorithm for pitch extraction from noisy speech signals based on both temporal and spectral representations is presented. We derive a harmonic sinusoidal correlation (HSC) model of clean speech as a temporal representation. Given only a noisy speech frame, a noise-robust least-squares minimization technique is proposed to acquire the parameters of the HSC model which are directly employed for the accurate estimation of a pitch-harmonic (PH). Exploiting the extracted PH and based on a spectral representation which is an enhanced spectrum in the discrete cosine transform domain, a two-fold criterion is developed in order to achieve the true consecutive number corresponding to PH that is finally adopted for pitch detection in the presence of noise. Simulation results using the Keele pitch extraction reference database manifest that combining the multi cues obtained from the temporal as well as spectral representations, the proposed algorithm is able to achieve a superior efficacy in comparison to some of the existing methods from high to very low signal-to-noise ratio (SNR) levels.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".