Latent Variable Analysis and Signal Separation [electronic resource] : 12th International Conference, LVA/ICA 2015, Liberec, Czech Republic, August 25-28, 2015, Proceedings /

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Material type: Text

TextSeries: Theoretical Computer Science and General Issues ; 9237Publisher: Cham : Springer International Publishing : Imprint: Springer, 2015Edition: 1st ed. 2015Description: XVI, 532 p. 128 illus. online resourceContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9783319224824

Subject(s):

Additional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification:

006.4 23

LOC classification:

Q337.5
TK7882.P3

Online resources:

Click here to access online

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.

In: Springer Nature eBookSummary: 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.

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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.

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