We have devised a greedy method for finding solutions to the sparse Deconvolution Approach for the Mapping of Acoustic Sources inverse problem using a variant of Orthogonal Matching Pursuit. The algorithm has two stages, wherein the first stage consists of selecting a subset of the basis vectors iteratively via a regularized inverse of the point spread function, and the second stage consists of constructing point source solutions using this basis subset and its coefficients via hierarchical agglomerative clustering. We have evaluated the algorithm on both synthetic and real data, and show that the overall accuracy in terms of direction of arrival and reconstructed source power is better than four other state of the art methods.

Spherical microphone arrays are suited for phase mode processing, which complements spatio-temporal processing and often simplifies both the understanding and development of different beamforming techniques. Since a spherical geometry cannot benefit directly from methods specially developed for uniform linear arrays, the weight and algorithm design for spherical arrays is mostly optimized and found numerically. One of the exceptions is a recent study that develops the well-known Dolph-Chebyshev weights in the phase mode framework. We show that for the case of a symmetric response, the spherical and linear array geometries are one-to-one, related through a herein developed linear transformation. With this transformation, uniform linear array specific processing techniques become readily available in closed form for spherical array processing. Any uniform linear array weight design technique can be directly applied to spherical arrays. We also show how this transformation can be used to generate virtual uniform linear array data from spherical array data, enabling us to apply uniform linear array specific adaptive algorithms to spherical arrays.

In this paper we investigate the use of GPUs as digital beamformers. We specify a parallel implementation of a beamformer in time and frequency domain and measure its performance. We also give examples of the processing limits of NVIDIA Geforce 8800 GPU with respect to application parameters: number of sensors, sampling frequency, bandwidth, and number of simultaneous beams. The results are compared to those of algorithms similarly implemented on a Intel Xeon CPU. We find that the GPU is able to process a larger amount of information than the CPU, and that it can be used as a digital beamformer for arrays with a large number of elements sampled at high rates. Exact results are given for the abovementioned application parameters.