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Latent Variable Analysis and Signal Separation

Overview of attention for book
Cover of 'Latent Variable Analysis and Signal Separation'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Higher-Order Block Term Decomposition for Spatially Folded fMRI Data
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    Chapter 2 Modeling Parallel Wiener-Hammerstein Systems Using Tensor Decomposition of Volterra Kernels
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    Chapter 3 Fast Nonnegative Matrix Factorization and Completion Using Nesterov Iterations
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    Chapter 4 Blind Source Separation of Single Channel Mixture Using Tensorization and Tensor Diagonalization
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    Chapter 5 High-Resolution Subspace-Based Methods: Eigenvalue- or Eigenvector-Based Estimation?
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    Chapter 6 Speaker Tracking on Multiple-Manifolds with Distributed Microphones
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    Chapter 7 VAST: The Virtual Acoustic Space Traveler Dataset
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    Chapter 8 Sketching for Nearfield Acoustic Imaging of Heavy-Tailed Sources
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    Chapter 9 Acoustic DoA Estimation by One Unsophisticated Sensor
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    Chapter 10 Acoustic Source Localization by Combination of Supervised Direction-of-Arrival Estimation with Disjoint Component Analysis
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    Chapter 11 An Initialization Method for Nonlinear Model Reduction Using the CP Decomposition
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    Chapter 12 Audio Zoom for Smartphones Based on Multiple Adaptive Beamformers
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    Chapter 13 Complex Valued Robust Multidimensional SOBI
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    Chapter 14 Ego Noise Reduction for Hose-Shaped Rescue Robot Combining Independent Low-Rank Matrix Analysis and Multichannel Noise Cancellation
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    Chapter 15 Some Theory on Non-negative Tucker Decomposition
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    Chapter 16 A New Algorithm for Multimodal Soft Coupling
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    Chapter 17 Adaptive Blind Separation of Instantaneous Linear Mixtures of Independent Sources
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    Chapter 18 Source Separation, Dereverberation and Noise Reduction Using LCMV Beamformer and Postfilter
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    Chapter 19 Toward Rank Disaggregation: An Approach Based on Linear Programming and Latent Variable Analysis
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    Chapter 20 A Proximal Approach for Nonnegative Tensor Decomposition
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    Chapter 21 Psychophysical Evaluation of Audio Source Separation Methods
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    Chapter 22 On the Use of Latent Mixing Filters in Audio Source Separation
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    Chapter 23 Discriminative Enhancement for Single Channel Audio Source Separation Using Deep Neural Networks
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    Chapter 24 Audiovisual Speech Separation Based on Independent Vector Analysis Using a Visual Voice Activity Detector
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    Chapter 25 Monoaural Audio Source Separation Using Deep Convolutional Neural Networks
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    Chapter 26 On the Behaviour of the Estimated Fourth-Order Cumulants Matrix of a High-Dimensional Gaussian White Noise
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    Chapter 27 Caveats with Stochastic Gradient and Maximum Likelihood Based ICA for EEG
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    Chapter 28 Approximate Joint Diagonalization According to the Natural Riemannian Distance
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    Chapter 29 Gaussian Processes for Source Separation in Overdetermined Bilinear Mixtures
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    Chapter 30 Model-Independent Method of Nonlinear Blind Source Separation
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    Chapter 31 The 2016 Signal Separation Evaluation Campaign
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    Chapter 32 Multimodality for Rainfall Measurement
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    Chapter 33 Particle Flow SMC-PHD Filter for Audio-Visual Multi-speaker Tracking
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    Chapter 34 Estimation of the Intrinsic Dimensionality in Hyperspectral Imagery via the Hubness Phenomenon
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    Chapter 35 A Blind Identification and Source Separation Method Based on Subspace Intersections for Hyperspectral Astrophysical Data
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    Chapter 36 Estimating the Number of Endmembers to Use in Spectral Unmixing of Hyperspectral Data with Collaborative Sparsity
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    Chapter 37 Sharpening Hyperspectral Images Using Plug-and-Play Priors
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    Chapter 38 On Extracting the Cosmic Microwave Background from Multi-channel Measurements
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    Chapter 39 Kernel-Based NPLS for Continuous Trajectory Decoding from ECoG Data for BCI Applications
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    Chapter 40 On the Optimal Non-linearities for Gaussian Mixtures in FastICA
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    Chapter 41 Fast Disentanglement-Based Blind Quantum Source Separation and Process Tomography: A Closed-Form Solution Using a Feedback Classical Adapting Structure
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    Chapter 42 Blind Separation of Cyclostationary Sources with Common Cyclic Frequencies
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    Chapter 43 Adaptation of a Gaussian Mixture Regressor to a New Input Distribution: Extending the C-GMR Framework
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    Chapter 44 Efficient Optimization of the Adaptive ICA Function with Estimating the Number of Non-Gaussian Sources
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    Chapter 45 Feasibility of WiFi Site-Surveying Using Crowdsourced Data
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    Chapter 46 On Minimum Entropy Deconvolution of Bi-level Images
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    Chapter 47 A Joint Second-Order Statistics and Density Matching-Based Approach for Separation of Post-Nonlinear Mixtures
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    Chapter 48 Optimal Measurement Times for Observing a Brownian Motion over a Finite Period Using a Kalman Filter
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    Chapter 49 On Disjoint Component Analysis
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    Chapter 50 Accelerated Dictionary Learning for Sparse Signal Representation
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    Chapter 51 BSS with Corrupted Data in Transformed Domains
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    Chapter 52 Singing Voice Separation Using RPCA with Weighted $$l_{1}$$ -norm
  54. Altmetric Badge
    Chapter 53 Multimodal Approach to Remove Ocular Artifacts from EEG Signals Using Multiple Measurement Vectors
Overall attention for this book and its chapters
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (69th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

twitter
9 tweeters

Citations

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2 Dimensions

Readers on

mendeley
6 Mendeley
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Title
Latent Variable Analysis and Signal Separation
Published by
Lecture notes in computer science, January 2017
DOI 10.1007/978-3-319-53547-0
ISBNs
978-3-31-953547-0, 978-3-31-953546-3
Editors

Petr Tichavsky, Massoud Babaie-Zadeh, Olivier Michel J.J., Nadège Thirion-Moreau

Twitter Demographics

The data shown below were collected from the profiles of 9 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 2 33%
Student > Master 2 33%
Unknown 2 33%
Readers by discipline Count As %
Engineering 3 50%
Computer Science 1 17%
Unknown 2 33%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 January 2018.
All research outputs
#3,548,952
of 13,960,267 outputs
Outputs from Lecture notes in computer science
#1,732
of 7,426 outputs
Outputs of similar age
#79,603
of 264,913 outputs
Outputs of similar age from Lecture notes in computer science
#23
of 120 outputs
Altmetric has tracked 13,960,267 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 7,426 research outputs from this source. They receive a mean Attention Score of 4.4. This one has done well, scoring higher than 76% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 264,913 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 120 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.