Robust Pitch Estimation At Very Low SNR Exploiting Time and Frequency Domain Cues
Bibliographic record
Abstract
In this paper, we present a joint time/frequency domain approach for pitch estimation of speech at a very low SNR. The kernel of this approach lies in introducing a new function for detecting the time-domain cue by modifying the circular average magnitude difference function (CAMDF). By using the new function in conjunction with the half-wave rectified version of the autocorrelation function, the pitch-peak can be emphasized and the non-pitch peaks suppressed. To guarantee a robust pitch detection in noisy speech, a priori frequency-domain estimate of the dominant pitch-harmonic is extracted as an additional cue and is utilized to optimally match the pitch-peak in time-domain. The proposed approach is simulated using the Keele reference database. It is shown that the proposed method using joint time and frequency domain cues is able to give a superior accuracy relative to some of the existing methods even at a very low SNR of -10 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".