Latent Variable Analysis and Signal Separation [electronic resource] : 13th International Conference, LVA/ICA 2017, Grenoble, France, February 21-23, 2017, Proceedings /

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Material type: Text

TextSeries: Theoretical Computer Science and General Issues ; 10169Publisher: Cham : Springer International Publishing : Imprint: Springer, 2017Edition: 1st ed. 2017Description: XVI, 576 p. 174 illus. online resourceContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9783319535470

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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

In: Springer Nature eBookSummary: This book constitutes the proceedings of the 13th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2017, held in Grenoble, France, in Feburary 2017. The 53 papers presented in this volume were carefully reviewed and selected from 60 submissions. They were organized in topical sections named: tensor approaches; from source positions to room properties: learning methods for audio scene geometry estimation; tensors and audio; audio signal processing; theoretical developments; physics and bio signal processing; latent variable analysis in observation sciences; ICA theory and applications; and sparsity-aware signal processing. .

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This book constitutes the proceedings of the 13th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2017, held in Grenoble, France, in Feburary 2017. The 53 papers presented in this volume were carefully reviewed and selected from 60 submissions. They were organized in topical sections named: tensor approaches; from source positions to room properties: learning methods for audio scene geometry estimation; tensors and audio; audio signal processing; theoretical developments; physics and bio signal processing; latent variable analysis in observation sciences; ICA theory and applications; and sparsity-aware signal processing. .

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