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Blind Signal Separation by Synchronized Joint Diagonalization

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Latent Variable Analysis and Signal Separation (LVA/ICA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10891))

Abstract

Joint Diagonalization (JD) is a well-known method for blind signal separation (BSS) by exploiting the nonstationarity of signals. In this paper, we propose Synchronized Joint Diagonalization (SJD) that solves multiple JD problems simultaneously and tries to synchronize the activity of the same signal along the time axis over the multiple JD problems. SJD attains not only signal separation by the mechanism of JD but also permutation alignment by the synchronization when applied to frequency-domain BSS. Although the formulation of SJD starts from the minimization of multi-channel Itakura-Saito divergences between a covariance matrix and a diagonal matrix, the simplified cost function with the finest time blocks becomes similar to that of Independent Vector/Component Analysis (IVA/ICA). We discuss the relationship among SJD and existing techniques. Experimental results on speech separation are shown to demonstrate the behavior of these methods.

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Author information

Authors and Affiliations

  1. NTT Communication Science Laboratories, NTT Corporation, Kyoto, Japan

    Hiroshi Sawada

Authors
  1. Hiroshi Sawada
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Correspondence to Hiroshi Sawada .

Editor information

Editors and Affiliations

  1. Paul Sabatier University, Toulouse, France

    Yannick Deville

  2. Bar-Ilan University, Ramat Gan, Israel

    Sharon Gannot

  3. University of Surrey, Guildford, United Kingdom

    Russell Mason

  4. University of Surrey, Guildford, United Kingdom

    Mark D. Plumbley

  5. University of Surrey, Guildford, United Kingdom

    Dominic Ward

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Sawada, H. (2018). Blind Signal Separation by Synchronized Joint Diagonalization. In: Deville, Y., Gannot, S., Mason, R., Plumbley, M., Ward, D. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2018. Lecture Notes in Computer Science(), vol 10891. Springer, Cham. https://doi.org/10.1007/978-3-319-93764-9_21

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  • DOI: https://doi.org/10.1007/978-3-319-93764-9_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93763-2

  • Online ISBN: 978-3-319-93764-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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