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Record W1985721081 · doi:10.1109/ccece.2008.4564500

A novel method for real-time audio watermarking using wavelet transform

2008· article· en· W1985721081 on OpenAlex
Mahmood Movassagh, Ali Asghar Beheshti Shirazi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueConference proceedings - Canadian Conference on Electrical and Computer Engineering · 2008
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Steganography and Watermarking Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsDigital watermarkingComputer scienceWatermarkRobustness (evolution)ResamplingAudio signalNoise (video)Wavelet transformSpeech recognitionArtificial intelligenceWaveletComputer visionEmbeddingSpeech codingImage (mathematics)

Abstract

fetched live from OpenAlex

In this paper we propose a novel method for audio watermarking which is appropriate for real-time applications. Here the energy proportion of two successive low-middle frequency bands of each audio signal frame is computed and through the use of Index Modulation technique we embed the watermark bits into these proportion values. Low complexity is the most important characteristic of the proposed method making it appropriate for real-time applications. The method is a blind one, that is it does not need the original signal for extracting watermark bits. Experimental results have shown that it has a very good robustness against common watermarking attacks such as mp3 compression, low pass filtering, resampling, requantizing and adding noise.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.987
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.236
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it