Beamforming with microphone arrays for directional sources

Abstract

Beamforming is done with an array of sensors to achieve a directional or spatially-specific response by using a model of the arriving wavefront. Real acoustic sources may deviate from the conventional plane wave or monopole model, causing decreased array gain or a total breakdown of beamforming. An alternative to beamforming with the conventional source model is presented which avoids this by using a more general source model. The proposed method defines a set of “sub-beamformers,” each designed to respond to a different spatial mode of the source. The outputs of the individual sub-beamformers are combined in a weighted sum to give an overall output of better quality than that of a conventional (monopole) beamformer. It is shown that with appropriate weighting, the optimum array gain can be achieved. A simple method is demonstrated to estimate the weighted sum, based on the observed data. The variance and bias of the estimate in the presence of noise are evaluated. Simulation and experimentally measured results are shown for a simple directive source. In the experiment, the proposed method provides an array gain of about 11 dB while beamforming using a point source model achieves only −4 dB.

Experimental validation of a coprime linear microphone array for high-resolution direction-of-arrival measurements

“…[1][2][3][4] In particular, acoustic source localization based on microphone array signal processing can be used to locate gunfire or aircraft in defense and homeland-security applications, to localize noise sources in the design and manufacturing process of machines in the aerospace industry, 5 or to localize an unknown number of spatially distributed sound sources in spatially distributed noise. 6 All of these applications benefit from array designs and beamforming techniques that produce narrow beams.…”

Broadband implementation of coprime linear microphone arrays for direction of arrival estimation

“…Microphone arrays are powerful tools which can be used for spatial recording, isolating a signal from noise and reverberation, and locating sound sources. [1][2][3] Accuracy of source localization and array gain are improved with increasing number of array elements. Using acoustic vector sensors which combine sound pressure and particle velocity shows promise; [4][5][6] however, these sensors can be expensive.…”

“…There has been active research in the area of array implementation during the recent two decades which has seen progress regarding array configurations as well as processing algorithms. 1,2,[7][8][9][10] Random sampling is a type of sparse sensing that has been applied to array design, wherein irregular element distribution is used. However construction is non-trivial, as optimization is often computationally demanding, thus suboptimal solutions may be found using a number of heuristic algorithms such as the Monte Carlo, simulated annealing, golden section search, and conjugate gradient algorithms, or combinations thereof.…”

“…Recent developments in uniform linear array processing may still be leveraged by coprime array processing, due to the uniform nature of the coprime subarrays. These include investigations by Bouchard et al 1 which use a weighted combination of sub-beamformers, each using different spatial modes for the source model. Crocco and Trucco 7 propose a method of optimizing the mean adherence between the desired and actual directivity pattern (also known as beampattern or directional characteristic), that takes advantage of the relationship between the sampling interval and the element separation of a uniform linear array.…”

Acoustical Direction Finding with Time-Modulated Arrays

“…Some of the most common methods that achieve this use a receiver that electronically or physically scans space to find the Direction of Arrival (DoA). Electronic systems usually incorporate an array of sensors [1,2,3]. …”