A Pitch Detection Method for Speech Signals with Low Signal-to-Noise Ratio
Bibliographic record
Abstract
A new method for the pitch detection of speech severely degraded by a white noise is presented in this paper. We intend to incorporate a noise reduction approach based on a modified power spectral subtraction scheme to enhance the pre-processed speech prior to pitch estimation. The de-noised speech is then passed through an inverse filter, whose parameters are derived from the linear prediction (LP) analysis, yielding an output referred to as the LP residual. Since the LP residual is capable of delivering the knowledge of glottal closure events, it is utilized to propose a new average magnitude sum function (AMSF) and an average magnitude difference function (AMDF) both of which exhibit the periodicity at the pitch period. Exploiting the property that the AMDF shows a notch while the AMSF produces a peak, the AMDF is weighted by the reciprocal of the AMSF to reinforce the pitch-harmonic-notches in a heavy noise. Simulation results using the Keele database guarantee a superior pitch detection efficacy of the proposed approach for a white noise-corrupted speech compared to some of the existing methods at a very low signal-to-noise ratio (SNR).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".