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Latent Variable Analysis and Signal Separation

12th International Conference, LVA/ICA 2015, Liberec, Czech Republic, August 25-28, 2015, Proceedings

  • Conference proceedings
  • © 2015

Overview

Editors:
  1. Emmanuel Vincent

    1. Inria, Villers-les-Nancy, France

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  2. Arie Yeredor

    1. Tel Aviv University, Tel-Aviv, Israel

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  3. Zbyněk Koldovský

    1. Technical University of Libere, Liberec, Czech Republic

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  4. Petr Tichavský

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

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  • Up-to-date results
  • Includes supplementary material: sn.pub/extras

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

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

Included in the following conference series:

Conference proceedings info: LVA/ICA 2015.

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Table of contents (61 papers)

  1. Front Matter

    Pages I-XVI
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  2. Tensor-Based Methods for Blind Signal Separation

    1. Front Matter

      Pages 1-1
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    2. Stochastic and Deterministic Tensorization for Blind Signal Separation

      • Otto Debals, Lieven De Lathauwer
      Pages 3-13

    3. Block-Decoupling Multivariate Polynomials Using the Tensor Block-Term Decomposition

      • Philippe Dreesen, Thomas Goossens, Mariya Ishteva, Lieven De Lathauwer, Johan Schoukens
      Pages 14-21

    4. A Polynomial Formulation for Joint Decomposition of Symmetric Tensors of Different Orders

      • Pierre Comon, Yang Qi, Konstantin Usevich
      Pages 22-30

    5. Rank Splitting for CANDECOMP/PARAFAC

      • Anh-Huy Phan, Petr Tichavský, Andrzej Cichocki
      Pages 31-40

    6. Some Rank Conditions for the Identifiability of the Sparse Paralind Model

      • Sebastian Miron, David Brie
      Pages 41-48

    7. Tensors and Latent Variable Models

      • Mariya Ishteva
      Pages 49-55

    8. Tensor Factorisation Approach for Separation of Convolutive Complex Communication Signals

      • Samaneh Kouchaki, Saeid Sanei
      Pages 56-63

    9. Enhanced Tensor Based Semi-blind Estimation Algorithm for Relay-Assisted MIMO Systems

      • Jianshu Zhang, Ahmad Nimr, Kristina Naskovska, Martin Haardt
      Pages 64-72

  3. Deep Neural Networks for Supervised Speech Separation/Enhancement

    1. Front Matter

      Pages 73-73
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    2. Improving Deep Neural Network Based Speech Enhancement in Low SNR Environments

      • Tian Gao, Jun Du, Yong Xu, Cong Liu, Li-Rong Dai, Chin-Hui Lee
      Pages 75-82

    3. Noise Perturbation Improves Supervised Speech Separation

      • Jitong Chen, Yuxuan Wang, DeLiang Wang
      Pages 83-90

    4. Speech Enhancement with LSTM Recurrent Neural Networks and its Application to Noise-Robust ASR

      • Felix Weninger, Hakan Erdogan, Shinji Watanabe, Emmanuel Vincent, Jonathan Le Roux, John R. Hershey et al.
      Pages 91-99

    5. Adaptive Denoising Autoencoders: A Fine-Tuning Scheme to Learn from Test Mixtures

      • Minje Kim, Paris Smaragdis
      Pages 100-107

  4. Joint Analysis of Multiple Datasets, Data Fusion, and Related Topics

    1. Front Matter

      Pages 109-109
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    2. Joint Independent Subspace Analysis: A Quasi-Newton Algorithm

      • Dana Lahat, Christian Jutten
      Pages 111-118

    3. Joint Decompositions with Flexible Couplings

      • Rodrigo Cabral Farias, Jérémy Emile Cohen, Christian Jutten, Pierre Comon
      Pages 119-126

    4. Learning Coupled Embedding Using MultiView Diffusion Maps

      • Ofir Lindenbaum, Arie Yeredor, Moshe Salhov
      Pages 127-134

    5. Extraction of Temporal Patterns in Multi-rate and Multi-modal Datasets

      • Antoine Liutkus, Umut Şimşekli, A. Taylan Cemgil
      Pages 135-142
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  1. Latent Variable Analysis and Signal Separation

Keywords

About this book

This book constitutes the proceedings of the 12th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICS 2015, held in Liberec, Czech Republic, in August 2015. The 61 revised full papers presented – 29 accepted as oral presentations and 32 accepted as poster presentations – were carefully reviewed and selected from numerous submissions. Five special topics are addressed: tensor-based methods for blind signal separation; deep neural networks for supervised speech separation/enhancement; joined analysis of multiple datasets, data fusion, and related topics; advances in nonlinear blind source separation; sparse and low rank modeling for acoustic signal processing.

Editors and Affiliations

  • Inria, Villers-les-Nancy, France

    Emmanuel Vincent

  • Tel Aviv University, Tel-Aviv, Israel

    Arie Yeredor

  • Technical University of Libere, Liberec, Czech Republic

    Zbyněk Koldovský

  • The Czech Academy of Sciences, Prague, Czech Republic

    Petr Tichavský

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