A Sparse Auditory Envelope Representation with Iterative Reconstruction for Audio Coding
Bibliographic record
Abstract
1933–2009 iiiii Modern audio coding exploits the properties of the human auditory system to efficiently code speech and music signals. Perceptual domain coding is a branch of audio coding in which the signal is stored and transmitted as a set of parameters derived directly from the modeling of the human auditory system. Often, the perceptual representation is designed such that reconstruction can be achieved with limited resources but this usually means that some perceptually irrelevant information is included. In this thesis, we investigate perceptual domain coding by using a representation designed to contain only the audible information regardless of whether reconstruction can be performed efficiently. The perceptual representation we use is based on a multichannel Basilar membrane model, where each channel is decomposed into envelope and carrier components. We assume that the information in the carrier is also present in the envelopes and therefore discard the carrier components. The envelope components are sparsified using a transmultiplexing masking model and form our basic
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".