Bibliographic record
Abstract
The Paulstretch algorithm is a procedure for achieving aurally pleasing extreme time-stretches while avoiding the “phasiness” issues common to phase-vocoder-based implementations. The algorithm accomplishes this by randomizing the phase information where a typical phase-vocoder approach would perform phase-unwrapping or another method to preserve phase alignment. When performed without time-stretching, phase randomization is often referred to as whisperization. This allows for more extreme time-stretches than other approaches. Where most audio time-stretching is usually used for small adjustments, Paulstretch is regularly used to stretch audio by a factor of 5, 10, 20 or much more. This effect is popular for its aesthetic timbral effects and is regularly used in soundscapes, film/television scoring, and more. When time-stretching is performed at such extremes, however, it is unclear how randomizing the phase, different FFT window sizes, the stretch factor, and other variables combine to affect the reconstructed audio output. This study employs multiple audio analysis methods to examine the spectral, timbral, and perceptual effects of the Paulstretch algorithm.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".