Recherche

Afficher tous les résultats pour

Accueil

Personne

Rémi Gribonval

Cette personne a quitté l’EPFL

À propos
Confidentialité
Mentions légales

Graph Chatbot

Publications associées (37)

Page 2 sur 2

Nonnegative Matrix Factorization and Spatial Covariance Model for Under-Determined Reverberant Audio Source Separation

Pierre Vandergheynst, Rémi Gribonval

We address the problem of blind audio source separation in the under-determined and convolutive case. The contribution of each source to the mixture channels in the time-frequency domain is modeled by a zero-mean Gaussian random vector with a full rank cov ...

2010

Blind Audiovisual Separation based on Redundant Representations

Pierre Vandergheynst, Rémi Gribonval, Gianluca Monaci, Anna Llagostera Casanovas

In this work we present a method to perform a complete audiovisual source separation without need of previous information. This method is based on the assumption that sounds are caused by moving structures. Thus, an efficient representation of audio and vi ...

2008

Blind Audio-Visual Source Separation based on Sparse Redundant Representations

Pierre Vandergheynst, Rémi Gribonval, Gianluca Monaci, Anna Llagostera Casanovas

In this paper we propose a novel method which is able to detect and separate audio-visual sources present in a scene. Our method exploits the correlation between the video signal captured with a camera and a synchronously recorded one-microphone audio trac ...

2010

Compressive Sampling of Pulse Trains : Spread the Spectrum !

Pierre Vandergheynst, Rémi Gribonval, Laurent Jacques

In this paper we consider the problem of sampling far below the Nyquist rate signals that are sparse linear superpositions of shifts of a known, potentially wide-band, pulse. This signal model is key for applications such as Ultra Wide Band (UWB) communica ...

2009

Doubly sparse models for multiple filter estimation in sparse echoic environments

Pierre Vandergheynst, Rémi Gribonval

We consider the estimation of multiple time-domain sparse filters from echoic mixtures of several unknown sources, when the sources are sparse in the time-frequency domain. We propose a sparse filter estimation framework consisting of two steps: a) a clust ...

2012

MoTIF: An Efficient Algorithm for Learning Translation Invariant Dictionaries

Pierre Vandergheynst, Rémi Gribonval, Philippe Jost

The performances of approximation using redundant expansions rely on having dictionaries adapted to the signals. In natural high-dimensional data, the statistical dependencies are, most of the time, not obvious. Learning fundamental patterns is an alternat ...

2006

Learning Multi-Modal Dictionaries: Application to Audiovisual Data

Pierre Vandergheynst, Rémi Gribonval, Gianluca Monaci, Philippe Jost

This paper presents a methodology for extracting meaningful synchronous structures from multi-modal signals. Simultaneous processing of multi-modal data can reveal information that is unavailable when handling the sources separately. However, in natural hi ...

2006

Multichannel thresholding with sensing dictionaries

Pierre Vandergheynst, Rémi Gribonval, Karin Schnass

This paper shows introduces the use sensing dictionaries for p-thresholding, an algorithm to compute simultaneous sparse approximations of multichannel signals over redundant dictionaries. We do both a worst case and average case recovery analyses of this ...

2007

Blind Calibration for Phase Shifts in Compressive Systems

Rémi Gribonval, Gilles Puy

We consider a blind calibration problem in a compressed sensing measurement system in which each sensor introduces an unknown phase shift to be determined. We show that this problem can be approached similarly to the problem of phase retrieval from quadrat ...

2013

A low complexity orthogonal matching pursuit for sparse signal approximation with shift-invariant dictionaries

Pierre Vandergheynst, Rémi Gribonval

We propose a variant of Orthogonal Matching Pursuit (OMP), called LoCOMP, for scalable sparse signal approximation. The algorithm is designed for shift- invariant signal dictionaries with localized atoms, such as time-frequency dictionaries, and achieves a ...

2009

Blind Sensor Calibration in Sparse Recovery Using Convex Optimization

Rémi Gribonval, Gilles Puy

We investigate a compressive sensing system in which the sensors introduce a distortion to the measurements in the form of unknown gains. We focus on blind calibration, using measures performed on a few unknown (but sparse) signals. We extend our earlier s ...

2013

Dictionary Identifiability from Few Training Samples

Rémi Gribonval, Karin Schnass

This article treats the problem of learning a dictionary providing sparse representations for a given signal class, via

ell^1

minimisation, or more precisely the problem of identifying a dictionary

dico

from a set of training samples

Y

knowing that $ ...

2008

Blind Sensor Calibration in Sparse Recovery

Rémi Gribonval, Gilles Puy

We consider the problem of calibrating a compressed sensing measurement system under the assumption that the decalibration consists of unknown complex gains on each measure. We focus on blind calibration, using measures performed on a few unknown (but spar ...

2013

Fast orthogonal sparse approximation algorithms over local dictionaries

Pierre Vandergheynst, Rémi Gribonval

In this work we present a new greedy algorithm for sparse approximation called LocOMP. LocOMP is meant to be run on local dictionaries made of atoms with much shorter supports than the signal length. This notably encompasses shift-invariant dictionaries an ...

2011

Dictionary learning for the sparse modelling of atrial fibrillation in ECG signals

Pierre Vandergheynst, Jean-Marc Vesin, Rémi Gribonval

We propose a new method for ventricular cancellation and atrial modelling in the ECG of patients suffering from atrial fibrillation. Our method is based on dictionary learning. It extends both the average beat subtraction and the sparse source separation a ...

2009

Average case analysis of multichannel thresholding

Pierre Vandergheynst, Rémi Gribonval, Karin Schnass

This paper introduces p-thresholding, an algorithm to compute simultaneous sparse approximations of multichannel signals over redundant dictionaries. We work out both worst case and average case recovery analyses of this algorithm and show that the latter ...

2007

Average case analysis of multichannel sparse approximations using p- thresholding

Pierre Vandergheynst, Rémi Gribonval, Karin Schnass

This paper introduces

p

-thresholding, an algorithm to compute simultaneous sparse approximations of multichannel signals over redundant dictionaries. We work out both worst case and average case recovery analyses of this algorithm and show that the latte ...

2007