Sparsity Based Framework for Spatial Sound Reproduction in Spherical Harmonic Domain

Abstract

In this paper, a novel sparsity based framework is proposed for accurate spatial sound field reproduction in spherical harmonic domain. The proposed framework can effectively reduce the number of loudspeakers required to reproduce the desired sound field using higher order ambisonics (HOA) over a fixed listening area. Although H OA provides accurate reproduction of spatial sound, it has a disadvantage in terms of the restriction on the area of sound reproduction. This area can be increased with the increase in the number of loudspeakers during reproduction. In order to limit the use of a large number of loudspeakers the sparse nature of the weight vector in the HOA signal model is utilized in this work. The problem of obtaining the weight vector is first formulated as a constrained optimization problem which is difficult to solve due to orthogonality property of the spherical harmonic matrix. This problem is therefore reformulated to exploit the sparse nature of the weight vector. The solution is then obtained by using the Bregman iteration method. Experiments on sound field reproduction in free space using the proposed sparsity based method are conducted using loudspeaker arrays. Performance improvements are noted when compared to least squares and compressed sensing methods in terms of sound field reproduction accuracy, subjective, and objective evaluations.