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We consider the problem of calibrating a compressed sensing measurement system under the assumption that the decalibration consists in unknown gains on each measure. We focus on blind calibration, using measures performed on a few unknown (but sparse) signals. A naive formulation of this blind calibration problem, using 1 minimization, is reminiscent of(More)
The recent theory of compressive sensing leverages upon the structure of signals to acquire them with much fewer measurements than was previously thought necessary, and certainly well below the traditional Nyquist-Shannon sampling rate. However, most implementations developed to take advantage of this framework revolve around controlling the measurements(More)
—We analyze the sampling of solutions to the Helmholtz equation (e.g. sound fields in the harmonic regime) using a least-squares method based on approximations of the solutions by sums of Fourier-Bessel functions or plane waves. This method compares favorably to others such as Orthogonal Matching Pursuit with a Fourier dictionary. We show that using a(More)
Measuring the Room Impulse Responses within a finite 3D spatial domain can require a very large number of measurements with standard uniform sampling. In this paper, we show that, at low frequencies, this sampling can be done with significantly less measurements, using some modal properties of the room. At a given temporal frequency, a plane wave(More)
449 In this work, a sparsity promoting strategy is developed in order to jointly achieve two complementary tasks regarding sound sources: localization and identification. Here, the sources are assumed sparse in the spatial domain, and greedy algorithms are used for their joint localization and identification in terms of spherical harmonics components. We(More)
This paper describes a method to obtain a perceptually relevant sparse representation of a sound signal. Based on matching pursuit (MP) and recent psychoacoustic data on time-frequency masking measured with Gabor atoms, a perceptual matching pursuit (PMP) algorithm is proposed. To obtain a good match between the masking model and the signal representation,(More)
This paper presents a method for designing a robust open spherical microphone array that overcomes the typical problems of open sphere geometries at frequencies related to the zeros of the spherical Bessel functions. The proposed array structure uses only a few additional sampling points inside the spherical volume whose optimal positions are determined by(More)
We propose a method for narrowband localization of sources in an unknown reverberant field. A sparse model for the wavefield is introduced , derived from the physical equations. We compare two lo-calization algorithms that take advantage on the structured sparsity naturally present into the model : a greedy iterative scheme, and an 1 minimization method.(More)
—This article presents a design method for microphone arrays with arbitrary geometries. Based on a theoretical analysis and on the magic points method, it allows for the interpolation of a sound field in a generic convex domain with a limited number of microphones on a given frequency band. It is shown that only a few microphones are needed in the interior(More)