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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 &#x2113;<sub>1</sub> minimization, is(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)
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)
In this paper, we investigate the optimal ways to sample multichannel impulse responses, composed of a small number of exponentially damped sinusoids, under the constraint that the total number of samples is fixed &#x2014; for instance with limited storage / computational power. We compute Cram&#x00E9;r-Rao bounds for multichannel estimation of the(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)
—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)
Narrowband source localization gets extremely challenging in strong reverberation. When the room is perfectly known, some dictionary-based methods have recently been proposed, allowing source localization with few measurements. In this paper, we first show that, for these methods, the choice of frequencies is important as they fail to localize sources that(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 localization algorithms that take advantage on the structured sparsity naturally present into the model: a greedy iterative scheme, and an &#x2113;<sub>1</sub>(More)
—We study two cases of acoustic source localization in a reverberant room, from a number of point-wise narrowband measurements. In the first case, the room is perfectly known. We show that using a sparse recovery algorithm with a dictionary of sources computed a priori requires measurements at multiple frequencies. Furthermore, we study the choice of(More)