In order to control an acoustic field inside a target region, it is important to choose suitable positions of secondary sources (loudspeakers) and sensors (control points/microphones). This article provides an overview of state-of-the-art source and sensor placement methods in sound field control. Although the placement of both sources and sensors greatly affects control accuracy and filter stability, their joint optimization has not been thoroughly investigated in the acoustics literature. In this context, we reformulate five general source and/or sensor placement methods that can be applied for sound field control. We compare the performance of these methods through extensive numerical simulations in both narrowband and broadband scenarios.

This study proposes a principled method to jointly determine the placement of acoustic sources (loudspeakers) and sensors (control points/microphones) in sound field control. The goal of this setup is to efficiently produce a sound field using multiple loudspeakers, approximately matching a target sound field over a region of interest. Therefore, the loudspeaker and control-point placement problem can be seen as the problem of finding interpolating functions (associated with individual loudspeaker sound fields) and sampling points (corresponding to control points or microphones) to approximate the target sound field in the given domain. We here solve this problem using the empirical interpolation method, originally developed for the numerical analysis of partial differential equations. The proposed method enables a joint determination of loudspeaker and control-point placement, from a large set of candidate locations, independently of the desired sound field. Numerical simulation results indicate that accurate and stable sound field control can be achieved by the proposed method, with significantly better results than with random and regular placements.

In the most general case, source localization has to take into account the radiation pattern of the sources of interest. This is particularly important when the sensors surround the sources, and the sources are anisotropic, as is the case in several applications (EEG, speech, musical instruments, etc.). Cramér-Rao bounds for the joint estimation of the position of a source and its radiation pattern are computed for simple cases of sensor array geometries and source models, showing that a good match between the source and the model improves the Cramér-Rao bounds. It is also shown that, in general, using a model more complex than the source makes the Fisher information matrix singular. These results are supported by numerical simulations and physical interpretations.

In this paper, the localization of spatially distributed sources is considered. Based on the problem formulation of the Deconvolution Approach for the Mapping of Acoustic Sources (DAMAS), a criterion based on a convex optimization under sparsity constraint is proposed to locate the sources. Also an original method is given to recover the angular distributions and the power of the sources. Simulations executed in the scenario of a mixture of distributed and point sources illustrate the validation of the proposed approach compared to other methods.

We introduce a generalization of the MUSIC algorithm to treat block-sparse signals in a multi-measurement vector framework. We show, through theoretical analysis and numerical experiments, that the requirements in terms of number of snapshots and number of measurements depend not only on the sparsity and on the size of the blocks, but also on the rank of the matrices of coefficients for each block. We apply this algorithm to the localization of directive sources, which can be modeled by block-sparsity in a dictionary of multipoles, and show that it compares favorably to a greedy approach based on the same model.

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 the eigenmodes of the sphere for a given wave number interval. This novel approach minimizes the interpolation error inside the sphere. With illustrate this approach with the design of a 10-th order array using 130 microphones and discuss the simulation results with regard to commonly used error measures (white noise gain, condition number, and interpolation error), and show that the proposed array design compares favorably to previously suggested array designs.

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, a dictionary of Gabor atoms with variable sizes is chosen for MP. In the proposed method, the signal is first decomposed using MP and the masking model is applied on the resulting set of atoms. This allows for isolating the masked components from the residual. Experimental results show that exploiting time-frequency masking allows to remove more atoms than using only spectral masking. Additionally, accounting for masking effects between atoms of different sizes and at different times allows for sparser representations. The objective evaluation of the proposed PMP algorithm indicates imperceptible distortions.

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 approximation of the acoustic field leads to a sparse approximation, and therefore a compressed sensing framework can be used for its acquisition. This paper describes three different sparse models that can be constructed, and the corresponding estimation algorithms: two models that exploit the structured sparsity of the soundfield, with projections of the modes onto plane waves sharing the same wavenumber, and one that computes a sparse decomposition on a dictionary of independent plane waves with time / space variable separation. These models are compared numerically and experimentally, with an array of 120 microphones irregularly placed within a 2 ×2 ×2 m volume inside a room, with an approximate uniform distribution. One of the most challenging part is the design of estimation algorithms whose computational complexity remains tractable.

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 ℓ1minimization method. Both methods are validated in 2D on numerical simulations, and on experimental data with a chaotic-shaped plate. These results, robust with respect to the specific sampling of the field and to noise, show that this approach may be an interesting alternative to traditional approaches of source localization, when a large number of narrowband sensors are deployed.

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 ℓ1minimization, is reminiscent of blind source separation and dictionary learning, which are known to be highly non-convex and riddled with local minima. In the considered context, we show that in fact this formulation can be exactly expressed as a convex optimization problem, and can be solved using off-the-shelf algorithms. Numerical simulations demonstrate the effectiveness of the approach even for highly uncalibrated measures, when a sufficient number of (unknown, but sparse) calibrating signals is provided. We observe that the success/failure of the approach seems to obey sharp phase transitions.