The implementation of pyschoacoustical signal parameters in the wavelet domain
Bibliographic record
Abstract
Introducing Wavelet techniques into the psychoacoustical analysis of sound signals provides a powerful alternative to standard Fourier methods. In this paper the reformulation of existing psychoacoustical signal parameters using wavelet methods will be explored. A major motivation for this work is that traditional psychoacoustical signal analysis relies heavily on the Fourier transform to provide a frequency content representation of a time signal, but this frequency domain representation is not always accurate; especially for sounds with impactive components. These impactive events can have a more significant contribution to the calculation of psychoacoustical signal parameters by reformulating existing psychoacoustic parameters in the Wavelet domain that are dependent on the Fourier transform. To provide concrete examples of the results simulated “plucked string” sounds are analyzed with analogous Fourier and Wavelet domain signal parameters to demonstrate the difference in performance achieved using Wavelet methods for sounds which have impactive components.
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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 | 0.006 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".