Super-resolution of Audio Files Using Feed-forward Neural Networks for Music Storage and Transfer
Why this work is in the frame
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Bibliographic record
Abstract
In this report, a system for reducing the file size of an audio signal, and then performing super-resolution on the resultant signal to estimate the original, is proposed and designed. This design takes influence from the principles of audio sampling, as well as super-resolution systems designed for visual media, and is split into an encoder and a decoder. The encoder successfully reduces the file size of the audio file by a significant amount. The super-resolution-based decoder can also successfully generate a matching high-frequency audio track that can be combined with the encoded lossy audio in order to estimate the original audio with a reasonable degree of accuracy. While a number of improvements to the system can be made in the future, it shows great promise, as it accomplishes the goals it was designed to meet.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| 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 it