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.
Material type: TextLanguage: İngilizce Series: Theoretical Computer Science and General Issues ; 9237Publisher: Cham : Springer International Publishing : Imprint: Springer, 2015Edition: 1st ed. 2015Description: 1 online resourceContent type:- text
- computer
- online resource
- 9783319224824
- Optical pattern recognition
- Computer vision
- Computer simulation
- Computer software
- Computational complexity
- Software engineering
- 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 Systems
- Q337.5
- TK7882.P3
Item type | Current library | Home library | Collection | Call number | Status | Notes | Date due | Barcode | |
---|---|---|---|---|---|---|---|---|---|
E-Book | Merkez Kütüphane | Merkez Kütüphane | E-Kitap Koleksiyonu | TK7882.P3EBK (Browse shelf(Opens below)) | Geçerli değil-e-Kitap / Not applicable-e-Book | BİL | EBK00612 |
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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