Section: Partnerships and Cooperations

European Initiatives

FP7 Projects


Participants : Rémi Gribonval, Jules Espiau de Lamaestre, Sangnam Nam, Emmanuel Vincent, Nancy Bertin.

  • Title: Sparse Models, Algorithms and Learning for Large-scale data


  • Defi: FET Open

  • Instrument: Specific Targeted Research Project (STREP)

  • Duration: February 2009 - January 2012

  • Coordinator: Inria (France)

  • Others partners: Univ. Edimburg (UK), Queen Mary Univ. (UK), EPFL (CH), Technion Univ. (ISR)

  • See also: http://small-project.eu/

  • Abstract: The project has developed new foundational theoretical framework for dictionary learning, and scalable algorithms for the training of structured dictionaries.

  • Title: Projections, Learning and Sparsity for Efficient data processing.

  • Type: IDEAS ()

  • Instrument: ERC Starting Grant (Starting)

  • Duration: January 2012 - December 2016

  • Coordinator: Inria (France)

  • Principal investigator: Rémi Gribonval

  • Abstract: The Please ERC is focused on the extension of the sparse representation paradigm towards that of “sparse modeling”, with the challenge of establishing, strengthening and clarifying connections between sparse representations and machine learning

Collaborations in other European Programs

  • Program: Eureka - Eurostars

  • Project acronym: i3DMusic

  • Project title: Real-time Interative 3D Rendering of Musical Recordings

  • Duration: October 2010 - September 2013

  • Other partners: Audionamix (FR), Sonic Emotion (CH), École Polytechnique Fédérale de Lausanne (CH)

  • Abstract:The i3DMusic project (Real-time Interative 3D Rendering of Musical Recordings) has been setup with the SMEs Audionamix and Sonic Emotion and the academic partner EPFL to provide a system enabling real-time interactive respatialization of mono or stereo music content. This will be achieved through the combination of source separation and 3D audio rendering techniques. Metiss is responsible for the source separation work package, more precisely for designing scalable online source separation algorithms and estimating advanced spatial parameters from the available mixture.