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Latent Variable Analysis and Signal Separation

13th International Conference, LVA/ICA 2017, Grenoble, France, February 21-23, 2017, Proceedings

  • Conference proceedings
  • © 2017

Overview

Editors:
  1. Petr Tichavský

    1. Institute of Information Theory and Automation, Prague, Czech Republic

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  2. Massoud Babaie-Zadeh

    1. Sharif University of Technology, Tehran, Iran

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  3. Olivier J.J. Michel

    1. Grenoble-Alpes University, Grenoble, France

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  4. Nadège Thirion-Moreau

    1. Toulon University, Toulon, France

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 10169)

Part of the book sub series: Theoretical Computer Science and General Issues (LNTCS)

Included in the following conference series:

Conference proceedings info: LVA/ICA 2017.

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Table of contents (53 papers)

  1. Tensors and Audio

    1. Adaptive Blind Separation of Instantaneous Linear Mixtures of Independent Sources

      • Ondřej Šembera, Petr Tichavský, Zbyněk Koldovský
      Pages 172-181

    2. Source Separation, Dereverberation and Noise Reduction Using LCMV Beamformer and Postfilter

      • Ofer Schwartz, Sebastian Braun, Sharon Gannot, Emanuël A. P. Habets
      Pages 182-191

    3. Toward Rank Disaggregation: An Approach Based on Linear Programming and Latent Variable Analysis

      • Vincent Vigneron, Leonardo Tomazeli Duarte
      Pages 192-200

    4. A Proximal Approach for Nonnegative Tensor Decomposition

      • Xuan Vu, Caroline Chaux, Nadège Thirion-Moreau, Sylvain Maire
      Pages 201-210

    5. Psychophysical Evaluation of Audio Source Separation Methods

      • Andrew J. R. Simpson, Gerard Roma, Emad M. Grais, Russell D. Mason, Christopher Hummersone, Mark D. Plumbley
      Pages 211-221

  2. Audio Signal Processing

    1. Front Matter

      Pages 223-223
      Download chapter PDF

    2. On the Use of Latent Mixing Filters in Audio Source Separation

      • Laurent Girin, Roland Badeau
      Pages 225-235

    3. Discriminative Enhancement for Single Channel Audio Source Separation Using Deep Neural Networks

      • Emad M. Grais, Gerard Roma, Andrew J. R. Simpson, Mark D. Plumbley
      Pages 236-246

    4. Audiovisual Speech Separation Based on Independent Vector Analysis Using a Visual Voice Activity Detector

      • Pierre Narvor, Bertrand Rivet, Christian Jutten
      Pages 247-257

    5. Monoaural Audio Source Separation Using Deep Convolutional Neural Networks

      • Pritish Chandna, Marius Miron, Jordi Janer, Emilia Gómez
      Pages 258-266

  3. Theoretical Developments

    1. Front Matter

      Pages 267-267
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    2. On the Behaviour of the Estimated Fourth-Order Cumulants Matrix of a High-Dimensional Gaussian White Noise

      • Pierre Gouédard, Philippe Loubaton
      Pages 269-278

    3. Caveats with Stochastic Gradient and Maximum Likelihood Based ICA for EEG

      • Jair Montoya-Martínez, Jean-François Cardoso, Alexandre Gramfort
      Pages 279-289

    4. Approximate Joint Diagonalization According to the Natural Riemannian Distance

      • Florent Bouchard, Jérôme Malick, Marco Congedo
      Pages 290-299

    5. Gaussian Processes for Source Separation in Overdetermined Bilinear Mixtures

      • Denis G. Fantinato, Leonardo T. Duarte, Bertrand Rivet, Bahram Ehsandoust, Romis Attux, Christian Jutten
      Pages 300-309

    6. Model-Independent Method of Nonlinear Blind Source Separation

      • David N. Levin
      Pages 310-319

  4. Physics and Bio Signal Processing

    1. Front Matter

      Pages 321-321
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    2. The 2016 Signal Separation Evaluation Campaign

      • Antoine Liutkus, Fabian-Robert Stöter, Zafar Rafii, Daichi Kitamura, Bertrand Rivet, Nobutaka Ito et al.
      Pages 323-332

    3. Multimodality for Rainfall Measurement

      • Hagit Messer
      Pages 333-343

    4. Particle Flow SMC-PHD Filter for Audio-Visual Multi-speaker Tracking

      • Yang Liu, Wenwu Wang, Jonathon Chambers, Volkan Kilic, Adrian Hilton
      Pages 344-353
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  1. Latent Variable Analysis and Signal Separation

Keywords

About this book

This book constitutes the proceedings of the 13th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2017, held in Grenoble, France, in Feburary 2017. The 53 papers presented in this volume were carefully reviewed and selected from 60 submissions. They were organized in topical sections named: tensor approaches; from source positions to room properties: learning methods for audio scene geometry estimation; tensors and audio; audio signal processing; theoretical developments; physics and bio signal processing; latent variable analysis in observation sciences; ICA theory and applications; and sparsity-aware signal processing. 

Editors and Affiliations

  • Institute of Information Theory and Automation, Prague, Czech Republic

    Petr Tichavský

  • Sharif University of Technology, Tehran, Iran

    Massoud Babaie-Zadeh

  • Grenoble-Alpes University, Grenoble, France

    Olivier J.J. Michel

  • Toulon University, Toulon, France

    Nadège Thirion-Moreau

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