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On Digital Watermarking for Audio Signals

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Advanced Computing in Industrial Mathematics (BGSIAM 2017)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 793))

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Abstract

We investigate the possibility of embedding watermarks robust against compression in musical audio files. The process of embedding and retrieving a watermark can be regarded as a binary communication channel. We investigate its statistic to give recommendation how to choose the embedding parameters and what error correcting codes to be used. The investigation covers the case of AAC and MP3 compression. The described method of embedding is based on a combination of key-dependable dither modulation and Haar wavelet transform. We analyze the whole process for its embedding capacitate and robustness. A practical method for choosing the embedding parameters is proposed.

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References

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  3. MathWorks, https://www.mathworks.com/help/wavelet/index.html

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Acknowledgements

This work was partially supported by the National Science Fund of Bulgaria under Grant DFNI-I02/8.

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Authors and Affiliations

  1. Institute of Mathematics and Informatics, Bulgarian Academy of Sciences (IMI-BAS), Acad. G. Bonchev Str., Bl. 8, 1113, Sofia, Bulgaria

    Hristo Kostadinov

  2. IMI-BAS, Acad. G. Bonchev Str., Bl. 8, 1113, Sofia, Bulgaria

    Nikolai L. Manev

Authors
  1. Hristo Kostadinov
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  2. Nikolai L. Manev
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Corresponding author

Correspondence to Hristo Kostadinov .

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Editors and Affiliations

  1. Institute of Information and Communication Technology, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Krassimir Georgiev

  2. Faculty of Applied Mathematics and Informatics, Technical University of Sofia, Sofia, Bulgaria

    Michail Todorov

  3. Institute of Mathematics and Informatics and Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Ivan Georgiev

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Kostadinov, H., Manev, N.L. (2019). On Digital Watermarking for Audio Signals. In: Georgiev, K., Todorov, M., Georgiev, I. (eds) Advanced Computing in Industrial Mathematics. BGSIAM 2017. Studies in Computational Intelligence, vol 793. Springer, Cham. https://doi.org/10.1007/978-3-319-97277-0_19

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