Extending the phase vocoder with damped sinusoid atomic decomposition of transients
Bibliographic record
Abstract
Pitch-preserving time scale modification and time-preserving pitch modification of recorded sounds are integral effects in modern digital music production, and some implementation of these effects can be found in nearly all commercial digital audio production software.Recent research has led to improvements in the reduction of transient smearing artifacts in otherwise high-quality frequency domain time scaling (phase vocoder) algorithms, but many modern implementations still exhibit noticeable smoothing of very abrupt transients, especially for drastic time scale modifications.By using a sparse atomic decomposition method to create representations of the transients in an audio signal, the transient and steady-state content of the signal can be separated and processed separately.The phase vocoder can be used to modify only the steady-state content of the signal, preserving the fidelity of transients when using time scaling effects.Such an extension is introduced here, along with a working software implementation, which performs such feature-specific processing through the use of a damped sinusoid matching pursuit algorithm to represent and remove transients from an audio signal.A high-resolution transient onset detection algorithm is also presented, as well as a practical application of phase locking to a computationally efficient phase vocoder formulation.vi
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".