A novel spread spectrum audio watermarking scheme based on time-frequency characteristics
Bibliographic record
Abstract
In this paper, we present a novel audio watermarking scheme based on spread spectrum techniques that 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. Our content-based algorithm aims to satisfy and maximize both imperceptibility and robustness of the watermark. In addition, our 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, we then derive the required sound pressure level of the watermark. From these results, modulation is 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 our scheme is robust to common signal processing attacks including filtering, MP3 compression and noise addition.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".