Latent Variable Analysis and Signal Separation : 12th International Conference, LVA/ICA 2015, Liberec, Czech Republic, August 25-28, 2015, Proceedings / edited by Emmanuel Vincent, Arie Yeredor, Zbyněk Koldovský, Petr Tichavský.

Format:

Book

Contributor:

Vincent, Emmanuel (Research scientist), editor.

Yeredor, Arie, editor.

Koldovský, Zbyněk, editor.

Tichavský, Petr, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

LNCS sublibrary. Theoretical computer science and general issues ; SL 1, 9237.

Theoretical Computer Science and General Issues ; 9237

Language:

English

Subjects (All):

Pattern perception.

Optical data processing.

Computer simulation.

Algorithms.

Computer science--Mathematics.

Computer science.

Computers, Special purpose.

Pattern Recognition.

Image Processing and Computer Vision.

Simulation and Modeling.

Algorithm Analysis and Problem Complexity.

Discrete Mathematics in Computer Science.

Special Purpose and Application-Based Systems.

Local Subjects:

Pattern Recognition.

Image Processing and Computer Vision.

Simulation and Modeling.

Algorithm Analysis and Problem Complexity.

Discrete Mathematics in Computer Science.

Special Purpose and Application-Based Systems.

Physical Description:

1 online resource (XVI, 532 pages) : 128 illustrations.

Edition:

First edition 2015.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2015.

System Details:

text file PDF

Summary:

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.

Contents:

Tensor-based methods for blind signal separation

Deep neural networks for supervised speech separation/enhancment

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.

Other Format:

Printed edition:

ISBN:

978-3-319-22482-4

9783319224824

Access Restriction:

Restricted for use by site license.