Latent Variable Analysis and Signal Separation [[electronic resource] ] : 13th International Conference, LVA/ICA 2017, Grenoble, France, February 21-23, 2017, Proceedings / / edited by Petr Tichavský, Massoud Babaie-Zadeh, Olivier J.J. Michel, Nadège Thirion-Moreau

• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017
• Edizione: 1st ed. 2017.
• Descrizione fisica: 1 online resource (XVI, 576 p. 174 illus.)
• Disciplina: 621.3822
• Soggetto topico: Pattern recognition systems; Computer vision; Artificial intelligence; Computer simulation; Numerical analysis; Computer networks; Automated Pattern Recognition; Computer Vision; Artificial Intelligence; Computer Modelling; Numerical Analysis; Computer Communication Networks
• Persona (resp. second.): TichavskýPetr; Babaie-ZadehMassoud; MichelOlivier J.J; Thirion-MoreauNadège
• Nota di contenuto: Intro -- Preface -- Organization -- Contents -- Tensor Approaches -- Higher-Order Block Term Decomposition for Spatially Folded fMRI Data -- 1 Introduction -- 1.1 Notation -- 2 Tensorial fMRI Analysis -- 2.1 Canonical Polyadic Decomposition (CPD) -- 2.2 Tensor Probabilistic Independent Component Analysis (TPICA) -- 3 Block Term Decomposition (BTD) for fMRI -- 3.1 Uniqueness -- 4 Simulation Results -- 4.1 Simulation of a Perception Study -- 4.2 Multi-slice Simulation -- 5 Conclusions -- References -- Modeling Parallel Wiener-Hammerstein Systems Using Tensor Decomposition of Volterra Kernels -- 1 Introduction -- 2 Volterra Kernels, Tensors and Tensor Decomposition -- 2.1 The Volterra Model for Nonlinear Systems -- 2.2 From Polynomials to Tensors -- 2.3 Canonical Polyadic Decomposition -- 3 Parallel Wiener-Hammerstein as Tensor Decomposition -- 3.1 Wiener-Hammerstein as Structured Tensor Decomposition -- 3.2 Parallel Wiener-Hammerstein Structure -- 3.3 Coupled Tensor and Matrix Decompositions -- 4 Numerical Results -- 5 Conclusions -- References -- Fast Nonnegative Matrix Factorization and Completion Using Nesterov Iterations -- 1 Introduction -- 2 Standard NeNMF -- 3 Extending NeNMF to Missing Entries -- 3.1 Weigthed Extension of NeNMF -- 3.2 EM Extension of NeNMF -- 4 Performance of the Weighted NeNMF Extensions -- 5 Conclusion -- A Proof of Lemmas 1 and 2 -- References -- Blind Source Separation of Single Channel Mixture Using Tensorization and Tensor Diagonalization -- 1 Introduction -- 2 General Framework for BSS of Single Channel Mixture -- 3 Tensorization of Sinusoid Signals -- 3.1 Two-Way and Three-Way Foldings -- 3.2 Toeplitzation -- 4 Simulations -- 5 Conclusions -- A Appendix: Low-Rank Representation of the Sequence x(t) = tn -- References -- High-Resolution Subspace-Based Methods: Eigenvalue- or Eigenvector-Based Estimation?. ; 1 Introduction -- 2 Multilevel Hankel Matrices and Their Subspaces -- 2.1 Definition and Factorization -- 2.2 Shift Properties of Subspaces -- 3 ESPRIT-Type Algorithms for MH Matrices -- 3.1 N-D ESPRIT Algorithm -- 3.2 IMDF Algorithm -- 3.3 IMDF Based on Least Squares (IMDF LS) -- 4 Perturbation Analysis -- 4.1 Basic Expressions -- 4.2 IMDF Perturbations -- 4.3 IMDF LS Perturbations -- 4.4 Computing the First-Order Perturbation and Its Moments -- 5 Simulations -- 6 Conclusions -- References -- From Source Positions to Room Properties: Learning Methods for Audio Scene Geometry Estimation -- Speaker Tracking on Multiple-Manifolds with Distributed Microphones -- 1 Introduction -- 2 Problem Formulation -- 3 Multiple-Manifold Gaussian Process -- 4 Multiple-Manifold Speaker Tracking -- 5 Experimental Study -- 6 Conclusions -- References -- VAST: The Virtual Acoustic Space Traveler Dataset -- 1 Introduction -- 2 Dataset Design -- 2.1 General Principles -- 2.2 Room Simulation and Data Generation -- 2.3 Room Properties: Size and Surfaces -- 2.4 Reverberation Time -- 2.5 Source and Receiver Positions -- 2.6 Test Sets -- 3 Virtually Supervised Sound Source Localization -- 4 Conclusion -- References -- Sketching for Nearfield Acoustic Imaging of Heavy-Tailed Sources -- 1 Introduction -- 2 Mixture Model and -Stable Theory -- 2.1 Notation and Convolutive Model -- 2.2 Independent Isotropic -Stable Model for the Sources -- 2.3 The Levy Exponent and the Spatial Measure -- 3 Parameter Estimation -- 3.1 Sketching for the Levy Exponent -- 3.2 A Proposed NMF Algorithm to Determine -- 4 Evaluation -- 5 Conclusion -- References -- Acoustic DoA Estimation by One Unsophisticated Sensor -- 1 Introduction -- 2 Localization of Noise Sources -- 2.1 Geometrical Structure -- 2.2 Structure Quality -- 2.3 Conditions for Localization -- 3 Algorithms -- 3.1 Subspace Model. ; 3.2 Dictionary Model -- 4 Numerical Results -- 4.1 White Sources -- 4.2 Speech Sources -- 5 Conclusion -- References -- Acoustic Source Localization by Combination of Supervised Direction-of-Arrival Estimation with Disjoint Component Analysis -- 1 Introduction -- 2 Methods -- 2.1 Probabilistic Source Localization -- 2.2 Disjoint Component Analysis -- 2.3 Decomposition of Source Probability Map -- 2.4 Multi-channel Signal Enhancement -- 3 Experiments and Results -- 4 Summary and Discussion -- References -- Tensors and Audio -- An Initialization Method for Nonlinear Model Reduction Using the CP Decomposition -- 1 Introduction -- 2 Notations and Problem Statement -- 3 Finding an Appropriate Initialization -- 4 Simulations and Results -- 5 Case Study -- 6 Conclusion -- References -- Audio Zoom for Smartphones Based on Multiple Adaptive Beamformers -- 1 Introduction -- 2 Proposed Audio Zoom System -- 2.1 Target Sound Source Enhancement -- 2.2 Proposed Audio Zoom Effect Creation -- 3 Experiments -- 3.1 Experiment Setup -- 3.2 Result with Subjective Test -- 4 Conclusion -- References -- Complex Valued Robust Multidimensional SOBI -- 1 Introduction -- 2 Preliminaries -- 3 Algorithms -- 3.1 SOBI -- 3.2 Affine Equivariant SAM-SOBI -- 3.3 Multidimensional SOBI -- 3.4 Robust Multidimensional SOBI -- 4 Simulation Study -- 5 Conclusions -- References -- Ego Noise Reduction for Hose-Shaped Rescue Robot Combining Independent Low-Rank Matrix Analysis and Multichannel Noise Cancellation -- 1 Introduction -- 2 Hose-Shaped Rescue Robot and Ego Noise -- 2.1 Hose-Shaped Rescue Robot -- 2.2 Problem in Recording Speech -- 3 Overview of Independent Low-Rank Matrix Analysis -- 3.1 Formulation -- 3.2 Independent Low-Rank Matrix Analysis -- 4 Multichannel Noise Canceller -- 4.1 Conventional Method -- 4.2 Proposed Method -- 4.3 Flow of the Proposed Method -- 5 Experiment. ; 5.1 Conditions -- 5.2 Results -- 6 Conclusion -- References -- Some Theory on Non-negative Tucker Decomposition -- 1 Tensor Decomposition Models -- 1.1 Tucker Decompositions -- 1.2 Canonical Polyadic Decomposition -- 2 Propagating Non-negativity and Non-negative Rank Through NTD -- 2.1 Elements of Cone Theory -- 2.2 Working Hypotheses -- 2.3 Propagating the Non-negative Rank to the Core -- 2.4 Propagating Non-negativity to the Core -- 3 Simulations -- 3.1 Some Algorithms for NTD and NMF -- 3.2 Some Tests on the Outputs of Algorithms -- 4 Conclusion -- References -- A New Algorithm for Multimodal Soft Coupling -- 1 Introduction -- 2 Soft Coupling for NMF -- 2.1 NMF Model -- 2.2 Coupled NMF -- 3 The Proposed Algorithm -- 3.1 Update Rule for Updating H1 -- 3.2 Update Rule for Updating -- 4 Experimental Results -- 5 Conclusion -- References -- Adaptive Blind Separation of Instantaneous Linear Mixtures of Independent Sources -- 1 Introduction -- 2 Signal Model and Separation Performance Limits -- 3 Adaptive BSS Algorithms -- 3.1 Scaled Stochastic Natural Gradient Algorithm -- 3.2 Adaptive BGSEP -- 3.3 Adaptive BARBI -- 4 Experiments -- 5 Conclusion -- References -- Source Separation, Dereverberation and Noise Reduction Using LCMV Beamformer and Postfilter -- 1 Introduction -- 2 Problem Formulation -- 3 Optimal Multichannel Speaker Separation, Dereverberation and Noise Reduction -- 4 Estimation of the Late Reverberation PSD Matrix -- 4.1 Estimator Based on a Temporal Model -- 4.2 Estimator Based on a Spatial Model -- 5 Performance Evaluation -- 5.1 Setup -- 5.2 Results -- 6 Conclusions -- References -- Toward Rank Disaggregation: An Approach Based on Linear Programming and Latent Variable Analysis -- 1 Introduction -- 2 Rank Aggregation -- 2.1 Rank-Aggregation as an Optimization Problem -- 2.2 Criteria for Rank Aggregation. ; 2.3 Numerical Example: CAC 40 Ranking of the Top 10 French Companies -- 3 Towards Rank Disaggregation -- 3.1 Rank Disaggregation via a Multivariate Decomposition Approach -- 3.2 Numerical Experiment -- 4 Conclusion -- References -- A Proximal Approach for Nonnegative Tensor Decomposition -- 1 Introduction -- 2 Canonical Polyadic Decomposition of N-th Order Tensors -- 2.1 Model -- 2.2 Objective -- 3 Optimization Problem and Proximal Algorithm -- 3.1 Criterion Formulation, Assumptions and Properties -- 3.2 Proposed Algorithm -- 3.3 Criterion Choice: Related Gradient and Proximity Operators -- 4 Numerical Simulations: Application to 4-th Order CPD -- 5 Conclusion -- References -- Psychophysical Evaluation of Audio Source Separation Methods -- Abstract -- 1 Introduction -- 2 Method -- 3 Results: Analysis I - Psychophysical Correlation -- 4 Results: Analysis II - Comparison of Separation Methods -- 5 Conclusion and Discussion -- Acknowledgment -- References -- Audio Signal Processing -- On the Use of Latent Mixing Filters in Audio Source Separation -- 1 Introduction -- 2 Latent Mixing Filters and Estimation of Source Image -- 2.1 Principle -- 2.2 General Expression of the Source Image MMSE Estimator -- 2.3 The Gaussian Case -- 2.4 Inference of Source Image Using Metropolis Algorithm -- 3 Experiments -- 4 Conclusion -- References -- Discriminative Enhancement for Single Channel Audio Source Separation Using Deep Neural Networks -- 1 Introduction -- 2 Problem Formulation of Audio SCSS -- 3 DNNs for source separation and enhancement -- 3.1 Training DNN-A for Source Separation -- 3.2 Training DNN-B for Discriminative Enhancement -- 3.3 Testing DNN-A and DNN-B -- 4 Experiments and Discussion -- 5 Conclusion -- References -- Audiovisual Speech Separation Based on Independent Vector Analysis Using a Visual Voice Activity Detector -- 1 Introduction. ; 2 Mathematical Preliminaries.
• Sommario/riassunto: This book constitutes the proceedings of the 13th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2017, held in Grenoble, France, in Feburary 2017. The 53 papers presented in this volume were carefully reviewed and selected from 60 submissions. They were organized in topical sections named: tensor approaches; from source positions to room properties: learning methods for audio scene geometry estimation; tensors and audio; audio signal processing; theoretical developments; physics and bio signal processing; latent variable analysis in observation sciences; ICA theory and applications; and sparsity-aware signal processing. .
• Titolo autorizzato: Latent Variable Analysis and Signal Separation
• ISBN: 3-319-53547-1
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996466077503316
• Lo trovi qui: Univ. di Salerno
• Serie: Theoretical Computer Science and General Issues, . 2512-2029 ; ; 10169