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Latent Variable Analysis and Signal Separation : 12th International Conference, LVA/ICA 2015, Liberec, Czech Republic, August 25-28, 2015, Proceedings / edited by Emmanuel Vincent, Arie Yeredor, Zbyněk Koldovský, Petr Tichavský
Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: SpringerLink Bücher | Lecture notes in computer science ; 9237Publisher: Cham [u.a.] : Springer, 2015Edition: 1st ed. 2015Description: Online-Ressource (XVI, 532 p. 128 illus, online resource)ISBN:- 9783319224824
- Signalquelle
- Signaltrennung
- Blinde Identifikation Informationstheorie
- Latente Variable
- Faktorenanalyse
- Sprachverarbeitung
- Audiotechnik
- Computer Science
- Optical data processing
- Pattern recognition
- Computer science—Mathematics
- Special purpose computers
- Software engineering
- Computer software
- Computational complexity
- Optical pattern recognition
- Algorithms
- Pattern recognition systems
- Discrete mathematics
- Computers, Special purpose
- Computer science
- Computer vision
- Computer simulation
- 006.4
- Q337.5 TK7882.P3
- Q337.5
- TK7882.P3
Contents:
Summary: 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.Summary: 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.PPN: PPN: 1657928071Package identifier: Produktsigel: ZDB-2-SXCS | ZDB-2-LNC | ZDB-2-SCS | ZDB-2-SEB
Tensor-based methods for blind signal separationDeep 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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