Overview
- Includes supplementary material: sn.pub/extras
Part of the book series: Lecture Notes in Computer Science (LNCS, volume 10169)
Part of the book sub series: Theoretical Computer Science and General Issues (LNTCS)
Included in the following conference series:
Conference proceedings info: LVA/ICA 2017.
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Table of contents (53 papers)
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Tensor Approaches
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From Source Positions to Room Properties: Learning Methods for Audio Scene Geometry Estimation
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Tensors and Audio
Other volumes
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Latent Variable Analysis and Signal Separation
Keywords
- blind signal separation
- dereverbation and denoising
- independent component analysis
- speech and audio separation
- tensor decomposition
- brain-computer interface
- constrained optimization
- crowdsourcing
- deep neural networks
- hyperspectral imaging
- latent variable analysis
- linear programming
- machine learning
- model-independent
- multimodality
- neuroscience
- non-negative matrix factorization
- room simulation
- sparsity-promoting methods
About this book
Editors and Affiliations
Bibliographic Information
Book Title: Latent Variable Analysis and Signal Separation
Book Subtitle: 13th International Conference, LVA/ICA 2017, Grenoble, France, February 21-23, 2017, Proceedings
Editors: Petr Tichavský, Massoud Babaie-Zadeh, Olivier J.J. Michel, Nadège Thirion-Moreau
Series Title: Lecture Notes in Computer Science
DOI: https://doi.org/10.1007/978-3-319-53547-0
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing AG 2017
Softcover ISBN: 978-3-319-53546-3Published: 15 February 2017
eBook ISBN: 978-3-319-53547-0Published: 13 February 2017
Series ISSN: 0302-9743
Series E-ISSN: 1611-3349
Edition Number: 1
Number of Pages: XVI, 576
Number of Illustrations: 174 b/w illustrations
Topics: Pattern Recognition, Image Processing and Computer Vision, Artificial Intelligence, Simulation and Modeling, Numeric Computing, Computer Communication Networks