Oct 27

Preprint + code on Compressive K-Means

We have a new preprint on Compressive K-Means.

Read it on https://hal.inria.fr/hal-01386077

Download the code at http://sketchml.gforge.inria.fr/

Jul 08

(closed) PhD offer – Interactive Navigation for a Video Audio True Experience @ PANAMA, Inria Rennes

Details and appliication here

Jun 16

Paper + code on random sampling of bandlimited signals on graphs

Our paper on Random Sampling of Bandlimited Signals on Graphs has been accepted for publication in Applied and Computational Harmonic Analysis .

Read it on arXiv:1511.05118 or hal:hal-01320214

Download the code at http://grsamplingbox.gforge.inria.fr/

May 25

2016 Award for Outstanding Contributions in Neural Systems

Congratulations to Antoine Deleforge (new PANAMA team member), Florence Forbes (MISTIS team) and Radu Horaud (PERCEPTION team) who received the 2016 Hojjat Adeli Award for Outstanding Contributions in Neural Systems for their paper:

  • A. Deleforge, F. Forbes, and R. Horaud (2015), “Acoustic Space Learning for Sound-source Separation and Localization on Binaural Manifolds,” International Journal of Neural Systems, 25:1, 1440003 (21 pages)

The Award for Outstanding Contributions in Neural Systems established by World Scientific Publishing Co. in 2010, is awarded annually to the most innovative paper published in the previous volume/year of the International Journal of Neural Systems.

For more information concerning this paper please visit the page of the PERCEPTION team on Acoustic Space Learning on Binaural Manifolds (article download, Matlab code, datasets, etc.)

May 24

(closed) PhD offer – Estimating the Geometry of Audio Scenes Using Virtually-Supervised Learning @ Inria Rennes

Details and application here

May 22

Paper + code on Compressive Spectral Clustering

Our paper on Compressive Spectral Clustering has been accepted to ICML.

Read it on arXiv:1602.02018 or hal:hal-01320214

Download the code at http://cscbox.gforge.inria.fr

Apr 18

Available for download: FaµST code, to replace large dense matrices with computationally efficient approximations

FaµST (pronounce FAUST) yields computationnaly efficient approximations to large dense matrices that can speed up iterative solvers for large-scale linear inverse problems.

See details on the methodology behind FaµST and some other applications, in our paper Le Magoarou L. and Gribonval R., “Flexible multi-layer sparse approximations of matrices and applications”, Journal of Selected Topics in Signal Processing, 2016.

Download the code at  http://faust.gforge.inria.fr/ and … stay tuned for the upcoming C++ FaµST library.

Mar 03

Just published: special Issue on Multimodality and New Interactions in Music Signal Processing

The French-speaking journal “Traitement du Signal” publishes this trimester a special issue on the theme of Multimodality and New Interactions in Music Signal Processing (“Traitement Des Signaux Musicaux Multimodalité Et Nouvelles Interactions”), coordinated by Nancy BERTIN, Frédéric BIMBOT, Jules ESPIAU DE LAMAËSTRE and Anaïk OLIVERO, members or former members of PANAMA team.

All articles published in this issue include a one-page abstract in English.

Feb 26

(closed) Postdoc on Compressive Statistical Learning @ Inria Rennes

The PANAMA team @ Inria, Rennes, France is seeking highly qualified post-doctoral candidates  to contribute to the development of a general theoretic and algorithmic framework for compressive statistical learning, by leveraging concepts from compressive sensing and graph signal processing.

Candidates should hold a Ph.D. in applied mathematics, statistics, theoretical computer science, or mathematical signal processing.

Details and application form here.

Feb 15

Multi-channel BSS Locate toolbox released

Multi-channel BSS Locate is a Matlab toolbox to estimate direction of arrival (expressed both in azimuth and elevation) of multiple sources in a multi-channel audio signal recorded by an array of microphones. This toolbox implements the previous 8 angular spectrum methods available in BSS Locate toolbox.

Matlab toolbox:


Online version (no Matlab required):