Audio Watermarking Based on Fibonacci Numbers
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a novel high-capacity audio watermarking system to embed data and extract them in a bit-exact manner by changing some of the magnitudes of the FFT spectrum. The key idea is to divide the FFT spectrum into short frames and change the magnitude of the selected FFT samples using Fibonacci numbers. Taking advantage of Fibonacci numbers, it is possible to change the frequency samples adaptively. In fact, the suggested technique guarantees and proves, mathematically, that the maximum change is less than 61% of the related FFT sample and the average error for each sample is 25%. Using the closest Fibonacci number to FFT magnitudes results in a robust and transparent technique. On top of very remarkable capacity, transparency and robustness, this scheme provides two parameters which facilitate the regulation of these properties. The experimental results show that the method has a high capacity (700 bps to 3 kbps), without significant perceptual distortion (ODG is about -1) and provides robustness against common audio signal processing such as echo, added noise, filtering, and MPEG compression (MP3). In addition to the experimental results, the fidelity of suggested system is proved mathematically.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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