Audio watermarking time-frequency characteristics
Bibliographic record
Abstract
In this paper, a novel audio watermarking scheme based on spread spectrum techniques is proposed. This technique embeds a digital watermark within an audio signal using the instantaneous mean frequency (IMF) of the signal. Audio watermarking offers a solution to data piracy and helps to protect the rights of the artists and copyright holders. The proposed content-based algorithm aims to satisfy and maximize both imperceptibility and robustness of the watermark. In addition, the technique uses the short-time Fourier transform of the original audio signal to estimate a weighted IMF of the signal. Based on the masking properties of the psychoacoustic model, the required sound pressure level of the watermark is calculated. Modulation is then performed to produce a signal-dependent watermark that is imperceptible. The proposed method allows 25 bits to be embedded and recovered within a 5 second sample of an audio signal. Experimental results have shown that the scheme is robust to common signal processing attacks including filtering, MP3 compression, additive noise and resampling with a bit error rate in the range of 013%.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".