Alexis Benichoux

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This paper summarizes the audio part of the 2011 community-based 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)
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)
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 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)
– On s'intéresse à l'estimation de réponses acoustiques à partir de l'enregistrement simultané de plusieurs sources connues. Les techniques existantes nécessitent de pouvoir se ramener au cas où le nombre de sources, connues ou inconnues, est au plus égal au nombre de capteurs. Notre méthode s'affranchit de cette hypothèse dans le cas où les sources sont(More)
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)
This paper summarizes the bio part of the 2011 community based Signal Separation Evaluation Campaign (SiSEC2011). Two different data sets were given. In the first task, participants were asked to estimate the causal relations of underlying sources from simulated bivari-ate EEG data. In the second task, participants were asked to reconstruct signaling(More)
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)