Improving Tonality Measures for Audio Watermarking

  • Michael Arnold
  • Xiao-Ming Chen
  • Peter G. Baum
  • Gwenaël Doërr
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6958)

Abstract

Psychoacoustic models are routinely used in audio watermarking algorithms to adjust the changes induced by the watermarking process to the sensitivity of the ear. The performances of such models in audio watermarking applications are tightly related to the determination of tonal and noise-like components. In this paper, we present an improved tonality estimation and its integration into a psychoacoustic model. Instead of conventional binary classification, we exploit bi-modal prediction for more precise tonality estimation. Experimental results show improved robustness of the considered audio watermarking algorithm integrating the new tonality estimation, while preserving the high quality of the audio track.

Keywords

Audio Signal Critical Band Tonal Component Embed Watermark Audio Watermark 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Michael Arnold
    • 1
  • Xiao-Ming Chen
    • 1
  • Peter G. Baum
    • 1
  • Gwenaël Doërr
    • 1
  1. 1.Technicolor – Security & Content Protection LabsGermany

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