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150908 ||| eng |
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|a 9783319224824
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|a Vincent, Emmanuel
|e [editor]
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245 |
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|a Latent Variable Analysis and Signal Separation
|h Elektronische Ressource
|b 12th International Conference, LVA/ICA 2015, Liberec, Czech Republic, August 25-28, 2015, Proceedings
|c edited by Emmanuel Vincent, Arie Yeredor, Zbyněk Koldovský, Petr Tichavský
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250 |
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|a 1st ed. 2015
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260 |
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|a Cham
|b Springer International Publishing
|c 2015, 2015
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300 |
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|a XVI, 532 p. 128 illus
|b online resource
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0 |
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|a 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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653 |
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|a Computer science—Mathematics
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653 |
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|a Computer vision
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653 |
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|a Discrete Mathematics in Computer Science
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653 |
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|a Computer simulation
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653 |
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|a Algorithms
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653 |
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|a Computer Modelling
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653 |
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|a Computer Vision
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653 |
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|a Discrete mathematics
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653 |
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|a Special Purpose and Application-Based Systems
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653 |
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|a Automated Pattern Recognition
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653 |
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|a Computers, Special purpose
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653 |
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|a Pattern recognition systems
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700 |
1 |
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|a Yeredor, Arie
|e [editor]
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700 |
1 |
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|a Koldovský, Zbyněk
|e [editor]
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700 |
1 |
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|a Tichavský, Petr
|e [editor]
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041 |
0 |
7 |
|a eng
|2 ISO 639-2
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989 |
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|b Springer
|a Springer eBooks 2005-
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490 |
0 |
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|a Theoretical Computer Science and General Issues
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028 |
5 |
0 |
|a 10.1007/978-3-319-22482-4
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856 |
4 |
0 |
|u https://doi.org/10.1007/978-3-319-22482-4?nosfx=y
|x Verlag
|3 Volltext
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082 |
0 |
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|a 006.4
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520 |
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|a 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
|