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Compressive spherical beamforming (CSB) with spherical microphone arrays enjoys high spatial resolution and strong sidelobes suppression at medium to high frequencies. In this paper, the adaptive reweighting homotopy (ARH) algorithm is adapted to CSB, and the proposed method is abbreviated as ARH-CSB, which bypasses a priori signal-to-noise ratio (SNR) estimation. ARH-CSB iteratively solves the CSB model by determining the support set and adaptively searching for weights while estimating the source strength. Simulations and experiments demonstrate that ARH-CSB can effectively identify acoustic sources. Furthermore, compared to CSB solved by ℓ 1 -norm minimization algorithm and iterative reweighted ℓ 1 -norm minimization algorithm, ARH-CSB does not depend on a priori SNR and has better adaptability to low SNR, stronger robustness, higher weak sources quantification accuracy, and faster computation speed. [ABSTRACT FROM AUTHOR]

Measured cross-spectral matrices (CSMs) from a microphone array will in some cases be contaminated by severe incoherent noise signals in the individual channels. A typical example is flow noise generated in the individual microphones when measuring in a wind tunnel. Assuming stationary signals and performing long-time averaging, the contamination will be concentrated on the CSM diagonal. When the CSM is used for traditional frequency-domain beamforming, diagonal removal (DR) will avoid use of the diagonal. DR is effective at suppressing the contamination effects, but it also has some side effects. With other beamforming algorithms and in connection with acoustic holography, however, the diagonal of the CSM is needed. The present paper describes a method for removal of incoherent noise contamination from the CSM diagonal. The method formulates the problem as a semidefinite program, which is a convex optimization problem that can be solved very efficiently and with guaranteed convergence properties. A first numerical study investigates the question, whether the semidefinite program formulation will provide in all cases the desired output. A second numerical study investigates the limitations introduced by off-diagonal noise contributions due to finite averaging time. The results of that study are backed up by results from a practical measurement. [ABSTRACT FROM AUTHOR]

This paper outlines an approach for obtaining microphone array geometry for use in traffic noise analysis. The designed array has a smaller number of microphones compared to existing solutions, thereby reducing the cost of system realization. The array geometry is irregular, consists of 24 microphones, and was obtained by an optimization procedure that minimizes beampatterns sidelobes. Microphone position optimization was performed in the frequency band from 300 Hz to 2000 Hz, defined by traffic noise characteristics. The designed array is used in ongoing research dedicated to the analysis of angular distribution of incident traffic noise sound energy impeding a facade in urban environment. [ABSTRACT FROM AUTHOR]

Sound field reproduction is aimed at the reconstruction of a sound pressure field in an extended area using dense loudspeaker arrays. In some circumstances, sound field reproduction is targeted at the reproduction of a sound field captured using microphone arrays. Although methods and algorithms already exist to convert microphone array recordings to loudspeaker array signals, one remaining research question is how to control the spatial sparsity in the resulting loudspeaker array signals and what would be the resulting practical advantages. Sparsity is an interesting feature for spatial audio since it can drastically reduce the number of concurrently active reproduction sources and, therefore, increase the spatial contrast of the solution at the expense of a difference between the target and reproduced sound fields. In this paper, the application of the elastic-net cost function to sound field reproduction is compared to the lasso cost function. It is shown that the elastic-net can induce solution sparsity and overcomes limitations of the lasso: The elastic-net solves the nonuniqueness of the lasso solution, induces source clustering in the sparse solution, and provides a smoother solution within the activated source clusters. [ABSTRACT FROM AUTHOR]