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Speech Watermarking Method Using McAdams Coefficient Based on Random Forest Learning.

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This study introduces a novel speech watermarking technique utilizing the McAdams coefficient for secure communication. The method demonstrates robustness against common signal processing, ensuring data integrity.

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Area of Science:

  • Digital Signal Processing
  • Information Security
  • Acoustics

Background:

  • Speech watermarking is crucial for securing communication systems.
  • Existing methods face challenges in balancing robustness and imperceptibility.
  • Protecting digital speech content requires effective and reliable watermarking solutions.

Purpose of the Study:

  • To develop a novel speech watermarking method using the McAdams coefficient.
  • To ensure high imperceptibility and robustness against signal processing.
  • To enable blind detection of embedded watermarks.

Main Methods:

  • Utilized the McAdams coefficient for frequency harmonics adjustment.
  • Implemented a bit-inverse shifting technique for watermark embedding.
  • Developed a random forest classifier for blind watermark detection based on frequency harmonics features.

Main Results:

  • The proposed method achieved a payload of 16 bits per second (bps).
  • Demonstrated high imperceptibility and robustness under normal conditions.
  • Showed resilience against common signal processing operations like format conversion (Ogg, MP4).

Conclusions:

  • The developed speech watermarking method effectively meets imperceptibility and robustness requirements.
  • The technique offers a secure solution for protecting speech communication.
  • The use of McAdams coefficients and random forest classification proves effective for reliable watermarking.