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- Shoko Araki, Francesco Nesta, +4 authors Alexis Benichoux
- LVA/ICA
- 2012

This paper summarizes the audio part of the 2011 communitybased Signal Separation Evaluation Campaign (SiSEC2011). Four speech and music datasets were contributed, including datasets recorded in noisy or dynamic environments and a subset of the SiSEC2010 datasets. The participants addressed one or more tasks out of four source separation tasks, and the… (More)

- Alexis Benichoux, Emmanuel Vincent, Rémi Gribonval
- 2013 IEEE International Conference on Acoustics…
- 2013

We consider the problem of blind sparse deconvolution, which is common in both image and signal processing. To counter-balance the ill-posedness of the problem, many approaches are based on the minimization of a cost function. A well-known issue is a tendency to converge to an undesirable trivial solution. Besides domain specific explanations (such as the… (More)

- Alexis Benichoux
- 2013

- Alexis Benichoux, Laurent S. R. Simon, Emmanuel Vincent, Rémi Gribonval
- IEEE Transactions on Signal Processing
- 2014

We propose to acquire large sets of room impulse responses (RIRs) by simultaneously playing known source signals on multiple loudspeakers. We then estimate the RIRs via a convex optimization algorithm using convex penalties promoting sparsity and/or exponential amplitude envelope. We validate this approach on real-world recordings. The proposed algorithm… (More)

Convolutive source separation is often done in two stages: 1) estimation of the mixing filters and 2) estimation of the sources. Traditional approaches suffer from the ambiguities of arbitrary permutations and scaling in each frequency bin of the estimated filters and/or the sources, and they are usually corrected by taking into account some special… (More)

We consider the estimation of acoustic impulse responses from the simultaneous recording of several known sources. Existing techniques are restricted to the case where the number of sources is at most equal to the number of sensors. We relax this assumption in the case where the sources are known. To this aim, we propose statistical models of the filters… (More)

- Alexis Benichoux, Prasad Sudhakar, Frédéric Bimbot, Rémi Gribonval
- LVA/ICA
- 2012

- Alexis Benichoux, Prasad Sudhakar, Frédéric Bimbot, Rémi Gribonvalb
- 2012

Convolutive source separation is often done in two stages: 1) estimation of the mixing filters and 2) estimation of the sources. Traditional approaches suffer from the ambiguities of arbitrary permutations and scaling in each frequency bin of the estimated filters and/or the sources, and they are usually corrected by taking into account some special… (More)

- Alexis Benichoux, Emmanuel Vincent, Rémi Gribonval
- 2011 IEEE Workshop on Applications of Signal…
- 2011

We consider the estimation of multiple room impulse responses from the simultaneous recording of several known sources. Existing techniques are restricted to the case where the number of sources is at most equal to the number of sensors. We relax this assumption in the case where the sources are known. To this aim, we propose statistical models of the… (More)

- Alexis Benichoux, Thomas Blumensath
- 2014 22nd European Signal Processing Conference…
- 2014

We propose a new matrix recovery framework to partition brain activity using time series of resting-state functional Magnetic Resonance Imaging (fMRI). Spatial clusters are obtained with a new low-rank factorization algorithm that offers the ability to add different types of constraints. As an example we add a total variation type cost function in order to… (More)