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Denoising of Audio Data by Nonlinear Diffusion

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Scale Space and PDE Methods in Computer Vision (Scale-Space 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3459))

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Abstract

Nonlinear diffusion has long proven its capability for discontinuity-preserving removal of noise in image processing. We investigate the possibility to employ diffusion ideas for the denoising of audio signals. An important difference between image and audio signals is which parts of the signal are considered as useful information and noise. While small-scale oscillations in visual images are noise, they encode essential information in audio data. To adapt diffusion to this setting, we apply it to the coefficients of a wavelet decomposition instead of the audio samples themselves. Experiments demonstrate that the denoising results are surprisingly good in view of the simplicity of our approach. Nonlinear diffusion promises therefore to become a powerful tool also in audio signal processing.

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Author information

Authors and Affiliations

  1. Mathematical Image Analysis Group, Faculty of Mathematics and Computer Science, Bldg. 27, Saarland University, 66041, Saarbrücken, Germany

    Martin Welk, Achim Bergmeister & Joachim Weickert

Authors
  1. Martin Welk
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  2. Achim Bergmeister
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  3. Joachim Weickert
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science, Technion – Israel Institute of Technology, 32000, Haifa, Israel

    Ron Kimmel

  2. School of Mathematical Sciences, Tel Aviv University, 69978, Tel Aviv, Israel

    Nir A. Sochen

  3. Mathematical Image Analysis Group, Faculty of Mathematics and Computer Science, Saarland University, Building E1.1, 66041, Saarbrücken, Germany

    Joachim Weickert

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© 2005 Springer-Verlag Berlin Heidelberg

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Welk, M., Bergmeister, A., Weickert, J. (2005). Denoising of Audio Data by Nonlinear Diffusion. In: Kimmel, R., Sochen, N.A., Weickert, J. (eds) Scale Space and PDE Methods in Computer Vision. Scale-Space 2005. Lecture Notes in Computer Science, vol 3459. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11408031_51

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  • DOI: https://doi.org/10.1007/11408031_51

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25547-5

  • Online ISBN: 978-3-540-32012-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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