Latent Variable Analysis and Signal Separation [electronic resource] :9th International Conference, LVA/ICA 2010, St. Malo, France, September 27-30, 2010. Proceedings /
Contributor(s): Vigneron, Vincent [editor.] | Zarzoso, Vicente [editor.] | Moreau, Eric [editor.] | Gribonval, Rémi [editor.] | Vincent, Emmanuel [editor.] | SpringerLink (Online service).Material type: BookSeries: Lecture Notes in Computer Science: 6365Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2010.Description: XVIII, 655 p. 182 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783642159954.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
Speech and Audio Applications -- Convolutive Signal Separation -- The 2010 Signal Separation Evaluation Campaign (SiSEC2010) -- Audio -- Theory -- Telecom -- Tensor Factorizations -- Sparsity I -- Sparsity; Biomedical Applications -- Non-negativity; Image Processing Applications -- Tensors; Joint Diagonalization -- Sparsity II -- Biomedical Applications -- Emerging Topics.
This book constitutes the proceedings of the 9th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2010, held in St. Malo, France, in September 2010. The 25 papers presented were carefully reviewed and selected from over hundred submissions. The papers collected in this volume demonstrate that the research activity in the field continues to gather theoreticians and practitioners, with contributions ranging range from abstract concepts to the most concrete and applicable questions and considerations. Speech and audio, as well as biomedical applications, continue to carry the mass of the considered applications. Unsurprisingly the concepts of sparsity and non-negativity, as well as tensor decompositions, have become predominant, reflecting the strong activity on these themes in signal and image processing at large.