On extracting pitch from noisy speech signals based on spectral and temporal enhancement
Bibliographic record
Abstract
A new pitch extractor based on spectral and temporal enhancement of noisy speech signals is presented in this paper. A discrete cosine transform based modified power spectral subtraction scheme is developed and employed prior to pitch extraction in order to suppress the underlying non-stationary noise. The de-noised speech thus obtained 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 instants, exploiting its high correlation property, an average magnitude sum function as well as an average magnitude difference function are introduced. The periodicity property of both the functions is argued to be integrated and an enhanced temporal function is put forward for robust pitch extraction in a multi-talker babble noise scenario. The superior efficacy of the proposed pitch extractor relative to some of the existing ones is confirmed through simulation results using the Keele database.
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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".