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Masked Positive Semi-definite Tensor Interpolation

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Latent Variable Analysis and Signal Separation (LVA/ICA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9237))

Abstract

Time-frequency constrained interpolation of audio has proven to be an effective technique in removing a wide variety of acoustic disturbances. Traditionally these techniques assume that the signal is stationary for the duration of the interpolation, which limits the types of disturbances that can be addressed. In this paper we propose masked positive semi-definite tensor factorisation followed by a novel form of multi-channel spectral subtraction to solve the problem, and we demonstrate excellent results on some real-world examples. The proposed methods can remove disturbances that were previously considered highly challenging to interpolate, for example a burst of wind noise in a voice recording.

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Author information

Authors and Affiliations

  1. CEDAR Audio Ltd., 20 Home End, Fulbourn, Cambridge, CB21 5BS, UK

    Dave Betts

Authors
  1. Dave Betts
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Corresponding author

Correspondence to Dave Betts .

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Editors and Affiliations

  1. Inria, Villers-les-Nancy, France

    Emmanuel Vincent

  2. Tel Aviv University, Tel-Aviv, Israel

    Arie Yeredor

  3. Technical University of Libere, Liberec, Czech Republic

    Zbyněk Koldovský

  4. The Czech Academy of Sciences, Prague, Czech Republic

    Petr Tichavský

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Betts, D. (2015). Masked Positive Semi-definite Tensor Interpolation. In: Vincent, E., Yeredor, A., Koldovský, Z., Tichavský, P. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2015. Lecture Notes in Computer Science(), vol 9237. Springer, Cham. https://doi.org/10.1007/978-3-319-22482-4_52

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  • DOI: https://doi.org/10.1007/978-3-319-22482-4_52

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22481-7

  • Online ISBN: 978-3-319-22482-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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