Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Learning multimodal dictionaries.

Gianluca Monaci1, Philippe Jost, Pierre Vandergheynst

  • 1Signal Processing Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland. gianluca.monaci@epfl.ch

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|September 6, 2007
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Multi-plane vision transformer for hemorrhage classification using axial and sagittal MRI data.

Scientific reports·2026
Same author

Conditional deep learning model reveals translation elongation determinants during amino acid deprivation.

Communications biology·2025
Same author

MARBLE: interpretable representations of neural population dynamics using geometric deep learning.

Nature methods·2025
Same author

Exploring "dark-matter" protein folds using deep learning.

Cell systems·2024
Same author

Deep learning-assisted model-based off-resonance correction for non-Cartesian SWI.

Magnetic resonance in medicine·2023
Same author

Iterative static field map estimation for off-resonance correction in non-Cartesian susceptibility weighted imaging.

Magnetic resonance in medicine·2022
Same journal

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

GoP-based Quality Enhancement on Video Compression.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Align then Tensorize: Multi-Level Consistent Anchor Graph Learning for Scalable Multi-View Clustering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Beyond Fidelity: Diverse Image Synthesis via Retrieval-Augmented Diffusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles

This study introduces a new multimodal signal model using sparse decomposition. The method effectively identifies audio-video patterns for accurate sound source localization, outperforming existing algorithms.

Area of Science:

  • Signal Processing
  • Computer Vision
  • Multimodal Data Analysis

Background:

  • Real-world phenomena integrate multiple sensory inputs.
  • Simultaneous processing of multimodal data reveals hidden information.
  • Statistical dependencies in natural multimodal signals are often unclear.

Purpose of the Study:

  • To present a novel model for multimodal signals based on sparse decomposition.
  • To develop an algorithm for learning multimodal generating functions.
  • To apply the model to audiovisual data for pattern discovery and source localization.

Main Methods:

  • Sparse decomposition of multimodal signals over a dictionary of structures.
  • Iterative learning of multimodal generating functions.

Related Experiment Videos

  • Solving a generalized eigenvector problem for efficient parameter-free learning.

    • Main Results:

    • The algorithm successfully discovers underlying structures in audiovisual sequences.
    • The method effectively localizes sound sources in video data.
    • Performance surpasses state-of-the-art audiovisual localization algorithms, even with distractors.

    Conclusions:

    • The proposed sparse decomposition model offers deep insights into multimodal signal structure.
    • The developed algorithm is fast, flexible, and requires no user-defined parameters.
    • This approach significantly enhances audiovisual source localization capabilities.