Audio watermarking paper

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Contents:
  1. Demystifying Audio Watermarking, Fingerprinting and Modulation
  2. Robust Audio Watermarking by Using Low-Frequency Histogram
  3. Audio Watermarking by selvakarna - File Exchange - MATLAB Central
  4. BBC R&D navigation

Demystifying Audio Watermarking, Fingerprinting and Modulation

Our system, like most modern audio-watermarking systems, uses the spread-spectrum concept. Further, this energy spread makes the watermark robust to common audio processing procedures, such as mp3 compression.


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Also like other systems, ours builds watermarks from noise blocks of fixed duration. Each noise block introduces its own, distinct perturbation pattern to selected frequency components in the host audio signal.

Robust Audio Watermarking by Using Low-Frequency Histogram

The watermark consists of noise blocks strung together in a predetermined sequence, and it looks like background noise to someone who lacks the decoding key. In conventional watermarking, the key is simply the sequence of the noise blocks, and the detector looks for that sequence in the audio signal.

In the second-screen scenario, however, electrical noise in the speaker and microphone and interference from echoes and ambient noise during acoustic transmission distort the watermark, making detection more challenging. We solve both problems by dispensing with the reference copy of the noise pattern.

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Instead, we embed the same, relatively short noise pattern in the audio signal multiple times. Rather than compare the received signal to a reference pattern, we compare it to itself. Because the whole audio signal passes through the same acoustic environment, the separate instances of the noise pattern will be distorted in similar ways. That means that we can compare them directly, without having to do any complex echo cancellation or noise reduction.

The detector takes advantage of the distortion due to the acoustic channel, rather than combatting it.

Audio Watermarking by selvakarna - File Exchange - MATLAB Central

Our system, like most modern audio-watermarking systems, uses the spread-spectrum concept. Further, this energy spread makes the watermark robust to common audio processing procedures, such as mp3 compression. Also like other systems, ours builds watermarks from noise blocks of fixed duration. Each noise block introduces its own, distinct perturbation pattern to selected frequency components in the host audio signal.

Audiojungle WATERMARK REMOVER - Complete Sound Removal Tutorial

The watermark consists of noise blocks strung together in a predetermined sequence, and it looks like background noise to someone who lacks the decoding key. In conventional watermarking, the key is simply the sequence of the noise blocks, and the detector looks for that sequence in the audio signal. In the second-screen scenario, however, electrical noise in the speaker and microphone and interference from echoes and ambient noise during acoustic transmission distort the watermark, making detection more challenging.

We solve both problems by dispensing with the reference copy of the noise pattern. Instead, we embed the same, relatively short noise pattern in the audio signal multiple times.

BBC R&D navigation

Rather than compare the received signal to a reference pattern, we compare it to itself. Because the whole audio signal passes through the same acoustic environment, the separate instances of the noise pattern will be distorted in similar ways.


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That means that we can compare them directly, without having to do any complex echo cancellation or noise reduction. The detector takes advantage of the distortion due to the acoustic channel, rather than combatting it. Abstract Today's digital media have made the product digital content very flexible and diminished the cost of its distribution.

However, it contributes on piracy explosion as digital content can be duplicated and re-distributed at virtually no cost. Watermarking technology appears in order to protect the intellectual property and fight the piracy. It consists on embedding data like copyright labels inside a data source without changing its perceptual quality.