Empirical Mode Decomposition for Analysis and Filtering of Speech Signals
Bibliographic record
Abstract
Speech signals typically have a stationary interval of 20-30 ms. Due to this, most speech processing techniques split speech signals into segments shorter than the stationary interval to take advantage of the piecewise stationary property of speech. However, there is no way to guarantee that the segments coincide with the stationary timescales inherent in the signal. Furthermore, how do we analyze speech signals over lengths longer than the stationary time scales? Second, there is evidence of the presence of nonlinearities in speech data from the published literature. In this article, the analysis of speech signals, without restriction to stationary time scales, using empirical mode decomposition (EMD) is presented in which the signal is broken down into components called intrinsic mode functions. EMD is especially suited for nonstationary and nonlinear data. The utility of this method, its effects, and opportunities for further research in the context of speech signals are presented.
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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".