A robust digital audio watermarking algorithm using Empirical Mode Decomposition
Bibliographic record
Abstract
In this paper, we proposed a new algorithm for digital audio watermarking process using Empirical Mode Decomposition (EMD) and Hilbert Transform (HT). The host audio signal is decomposed into several Intrinsic Mode Functions (IMFs). A set of -1 and 1, which is obtained by mapping from standard normal distributed pseudo random numbers, is embedded as secret information into the IMF containing highest energy. Thus selected IMF is less sensitive with common signal processing attack. As a result this method increases robustness. The experimental results show that the proposed method has good imperceptibility and robustness under common signal processing attacks such as additive noise (in time domain and in frequency domain), low pass filtering, re-sampling, re-quantization, MP3 compression, and sound processing effects such as, delay, a natural sounding reverberation (Schroeder's Reverberator), flanging effect, equalization effect.
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How this classification was reachedexpand
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 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".