Latent Variable Analysis and Signal Separation [electronic resource] : 12th International Conference, LVA/ICA 2015, Liberec, Czech Republic, August 25-28, 2015, Proceedings / edited by Emmanuel Vincent, Arie Yeredor, Zbyněk Koldovský, Petr Tichavský.
Material type: TextSeries: Theoretical Computer Science and General Issues ; 9237 | Lecture Notes in Computer Science ; 9237Publisher: Cham : Springer International Publishing : Imprint: Springer, 2015Edition: 1st ed. 2015Description: XVI, 532 p. 128 illus. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783319224824Subject(s): Pattern recognition | Optical data processing | Computer simulation | Algorithms | Computer science—Mathematics | Special purpose computers | 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 SystemsAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 006.4 LOC classification: Q337.5TK7882.P3Online resources: Click here to access onlineCurrent library | Home library | Call number | Materials specified | URL | Status | Date due | Barcode |
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IMSc Library | IMSc Library | Link to resource | Available | EBK14325 |
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.
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