VARIABLE PRE-EMPHASIS LPC FOR MODELING VOCAL EFFORT IN THE SINGING VOICE
Bibliographic record
Abstract
In speech and singing, the spectral envelope of the glottal source varies according to different voice qualities such as vocal effort, lax voice, and breathy voice. In contrast, linear prediction coding (LPC) models the glottal source in a way that is not flexible. The spectral envelope of the source estimated by LPC is fixed and determined by the pre-emphasis filter. In standard LPC, the formant filter captures variation in the spectral envelope that should be associated with the source. This paper presents variable preemphasis LPC (VPLPC) as a technique to allow the estimated source to vary. This results in formant filters that remain more consistent across variations in vocal effort and breathiness. VPLPC also provides a way to change the envelope of the estimated source, thereby changing the perception of vocal effort. The VPLPC algorithm is used to manipulate some voice excerpts with promising but mixed results. Possible improvements are suggested. 1.
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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 | 0.000 |
| 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".