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MULAN: A Blind and Off-Grid Method for Multichannel Echo Retrieval

Publications associées (54)

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Filtered Variation method for denoising and sparse signal processing

Volkan Cevher

We propose a new framework, called Filtered Variation (FV), for denoising and sparse signal processing applications. These problems are inherently ill-posed. Hence, we provide regularization to overcome this challenge by using discrete time filters that ar ...

2012

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

Effectively Modeling Data from Large-area Community Sensor Networks

Karl Aberer, Dipanjan Chakraborty, Saket Sathe, Sebastian Cartier

Effectively managing the data generated by Large-area Community driven Sensor Networks (LCSNs) is a new and challenging prob- lem. One important step for managing and querying such sensor network data is to create abstractions of the data in the form of mo ...

Assoc Computing Machinery2012

A Laplacian pyramid scheme in graph signal processing

Mohammadjavad Faraji

In this report we extend the ideas behind classical multiscale signal processing techniques in order to analyze data residing on graphs. In particular, we extend the notions of filtering, downsampling, and upsampling to functions defined on graphs. We then ...

2011

A Wideband Doubly-Sparse Approach For MITO Sparse Filter Estimation

Rémi Gribonval

We propose an approach for the estimation of sparse filters from a convolutive mixture of sources, exploiting the time-domain sparsity of the mixing filters and the sparsity of the sources in the time-frequency (TF) domain. The proposed approach is based o ...

IEEE2011

Signal Processing in Space and Time

Francisco Pereira Correia Pinto

Sound waves propagate through space and time by transference of energy between the particles in the medium, which vibrate according to the oscillation patterns of the waves. These vibrations can be captured by a microphone and translated into a digital sig ...

EPFL2010

Frame Domain Signal Processing: Framework and Applications

Martin Vetterli, Amina Chebira

Besides basis expansions, frames representations play a key role in signal processing. We thus consider the problem of frame domain signal processing, which is more complex and challenging than transform domain processing. Examples of such processing aboun ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2010

Space/time-frequency processing of acoustic wave fields: theory, algorithms, and applications

Martin Vetterli, Francisco Pereira Correia Pinto

Consider a nonparametric representation of acoustic wave fields that consists of observing the sound pressure along a straight line or a smooth contour L defined in space. The observed data contains implicit information of the surrounding acoustic scene, b ...

Institute of Electrical and Electronics Engineers2010

Mixed-domain system representation using Volterra series

Martin Hasler

Mixed-signal systems combine analog and digital signal processing in one overall design that should be integrated as a single system on chip. At system level, the separation into isolated subsystem representations for analog and digital parts needs to be o ...

IEEE2008

Performance of Sigma-Delta Quantizations in Finite Frames

In this paper, we consider sigma-delta (SD) quantization of geometrically uniform (GU) finite frames. In the first part, we prove that under some conditions, the variant I and II permutation modulation (PM) codes, first introduced by Slepian (1968, 1965), ...

2008