Latent Variable Analysis and Signal Separation [electronic resource] :12th International Conference, LVA/ICA 2015, Liberec, Czech Republic, August 25-28, 2015, Proceedings /
Contributor(s): Vincent, Emmanuel [editor.] | Yeredor, Arie [editor.] | Koldovský, Zbyněk [editor.] | Tichavský, Petr [editor.] | SpringerLink (Online service).Material type: BookSeries: Lecture Notes in Computer Science: 9237Publisher: Cham : Springer International Publishing : Imprint: Springer, 2015.Edition: 1st ed. 2015.Description: XVI, 532 p. 128 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319224824.Subject(s): Computer science | Special purpose computers | Algorithms | Computer science -- Mathematics | Computer simulation | Image processing | Pattern recognition | Computer Science | Pattern Recognition | Image Processing and Computer Vision | Simulation and Modeling | Algorithm Analysis and Problem Complexity | Discrete Mathematics in Computer Science | Special Purpose and Application-Based SystemsOnline resources: Click here to access online
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.
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.