Latent Variable Analysis and Signal Separation: 12th International Conference, LVA/ICA 2015, Liberec, Czech Republic, August 25-28, 2015, Proceedings
Author:   Emmanuel Vincent ,  Arie Yeredor ,  Zbyněk Koldovský ,  Petr Tichavský
Publisher:   Springer International Publishing AG
Edition:   1st ed. 2015
Volume:   9237
ISBN:  

9783319224817


Pages:   532
Publication Date:   18 August 2015
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Latent Variable Analysis and Signal Separation: 12th International Conference, LVA/ICA 2015, Liberec, Czech Republic, August 25-28, 2015, Proceedings


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Overview

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.

Full Product Details

Author:   Emmanuel Vincent ,  Arie Yeredor ,  Zbyněk Koldovský ,  Petr Tichavský
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   1st ed. 2015
Volume:   9237
Dimensions:   Width: 15.50cm , Height: 2.80cm , Length: 23.50cm
Weight:   0.831kg
ISBN:  

9783319224817


ISBN 10:   3319224816
Pages:   532
Publication Date:   18 August 2015
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of 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.

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