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Latent Variable Analysis and Signal Separation

14th International Conference, LVA/ICA 2018, Guildford, UK, July 2–5, 2018, Proceedings

  • Yannick Deville
  • Sharon Gannot
  • Russell Mason
  • Mark D. Plumbley
  • Dominic Ward
Conference proceedings LVA/ICA 2018

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

Also part of the Theoretical Computer Science and General Issues book sub series (LNTCS, volume 10891)

Table of contents

  1. Front Matter
    Pages I-XVII
  2. Structured Tensor Decompositions and Applications

    1. Front Matter
      Pages 1-1
    2. Xu Han, Laurent Albera, Amar Kachenoura, Huazhong Shu, Lotfi Senhadji
      Pages 3-12
    3. Mathieu Fontaine, Fabian-Robert Stöter, Antoine Liutkus, Umut Şimşekli, Romain Serizel, Roland Badeau
      Pages 13-23
    4. Gökhan Çapan, Semih Akbayrak, Taha Yusuf Ceritli, Ali Taylan Cemgil
      Pages 24-35
    5. Jeremy Emile Cohen, Rodrigo Cabral Farias, Bertrand Rivet
      Pages 36-45
    6. Pedro Marinho R. de Oliveira, Vicente Zarzoso
      Pages 46-56
    7. Mohamad Jouni, Mauro Dalla Mura, Pierre Comon
      Pages 57-66
  3. Matrix and Tensor Factorizations

    1. Front Matter
      Pages 67-67
    2. Emilie Renard, Kyle A. Gallivan, P.-A. Absil
      Pages 69-78
    3. Philippe Dreesen, Jeroen De Geeter, Mariya Ishteva
      Pages 79-88
    4. Jeremy E. Cohen, Rasmus Bro
      Pages 89-98
    5. Ivan Markovsky, Antonio Fazzi, Nicola Guglielmi
      Pages 99-106
    6. Stéphane Chrétien, Olivier Ho
      Pages 127-138
    7. Guillaume Olikier, P.-A. Absil, Lieven De Lathauwer
      Pages 139-148
  4. ICA Methods

    1. Front Matter
      Pages 149-149
    2. Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort
      Pages 151-160
  5. Nonlinear Mixtures

    1. Front Matter
      Pages 181-181
    2. Andréa Guerrero, Yannick Deville, Shahram Hosseini
      Pages 183-192
    3. Denis G. Fantinato, Leonardo T. Duarte, Yannick Deville, Christian Jutten, Romis Attux, Aline Neves
      Pages 193-203
  6. Audio Data and Methods

    1. Front Matter
      Pages 215-215
    2. Christopher Schymura, Peng Guo, Yanir Maymon, Boaz Rafaely, Dorothea Kolossa
      Pages 228-237
    3. Ryan M. Corey, Andrew C. Singer
      Pages 238-247
    4. Markus Matilainen, Klaus Nordhausen, Joni Virta
      Pages 248-258
    5. William J. Wilkinson, Joshua D. Reiss, Dan Stowell
      Pages 259-269
    6. Jaroslav Čmejla, Tomáš Kounovský, Jiří Málek, Zbyněk Koldovský
      Pages 270-279
    7. Delia Fano Yela, Dan Stowell, Mark Sandler
      Pages 280-289
  7. Signal Separation Evaluation Campaign (SiSEC 2018)

    1. Front Matter
      Pages 291-291
    2. Fabian-Robert Stöter, Antoine Liutkus, Nobutaka Ito
      Pages 293-305
    3. Gerard Roma, Owen Green, Pierre Alexandre Tremblay
      Pages 306-315
  8. Deep Learning and Data-driven Methods

    1. Front Matter
      Pages 317-317
    2. Daniel Stoller, Sebastian Ewert, Simon Dixon
      Pages 329-339
    3. Emad M. Grais, Hagen Wierstorf, Dominic Ward, Mark D. Plumbley
      Pages 340-350
    4. Alfredo Zermini, Qiuqiang Kong, Yong Xu, Mark D. Plumbley, Wenwu Wang
      Pages 361-371
    5. Sefik Emre Eskimez, Ross K. Maddox, Chenliang Xu, Zhiyao Duan
      Pages 372-381
  9. Advances in Phase Retrieval and Applications

    1. Front Matter
      Pages 383-383
    2. Guillaume Beaumont, Ronan Fablet, Angélique Drémeau
      Pages 385-394
    3. Christopher A. Metzler, Philip Schniter, Richard G. Baraniuk
      Pages 395-406
    4. A. Marina Krémé, Valentin Emiya, Caroline Chaux
      Pages 417-426
  10. Sparsity-Related Methods

    1. Front Matter
      Pages 427-427
    2. Pavel Záviška, Pavel Rajmic, Zdeněk Průša, Vítězslav Veselý
      Pages 429-445

About these proceedings

Introduction

This book constitutes the proceedings of the 14th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2018, held in  Guildford, UK, in July 2018.
The 52 full papers  were carefully reviewed and selected from 62 initial submissions. 
As research topics the papers encompass a wide range of general mixtures of latent variables models but also theories and tools drawn from a great variety of disciplines such as structured tensor decompositions and applications; matrix and tensor factorizations; ICA methods; nonlinear mixtures; audio data and methods; signal separation evaluation campaign; deep learning and data-driven methods; advances in phase retrieval and applications; sparsity-related methods; and biomedical data and methods.

Keywords

Source separation Non-negative matrix factorization Audio Biomedical signal processing Matrix and tensor decompositions Nonlinear mixtures Independent component analysis Bayesian methods Sparsity-related methods Deep learning Blind source separation Speech recognition Speech analysis speech processing Factorization Image reconstruction Neural networks

Editors and affiliations

  1. 1.Paul Sabatier UniversityToulouseFrance
  2. 2.Bar-Ilan UniversityRamat GanIsrael
  3. 3.University of SurreyGuildfordUnited Kingdom
  4. 4.University of SurreyGuildfordUnited Kingdom
  5. 5.University of SurreyGuildfordUnited Kingdom

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-93764-9
  • Copyright Information Springer International Publishing AG, part of Springer Nature 2018
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-93763-2
  • Online ISBN 978-3-319-93764-9
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site