Abstract
In designing acoustic broadband beamformers, the complexity can grow significantly when the number of microphones and the filter length increase. It is advantageous if many of the filter coefficients are zeroes so that the implementation can be executed with less computation. Moreover, the size of the array can also be pruned to reduce complexity. These problems are addressed in this paper. A suitable optimization model is proposed. Both array pruning and filter thinning can be solved together as a two-stage optimization problem to yield the final sparse designs. Numerical results show that the complexity of the designed beamformers can be reduced significantly with minimal effect on performance.
Highlights
Beamforming is a spatial filtering technique used to enhance the required signal via a sensor array for directional signal transmission or reception [1,2]
The beamformer design problem can be formulated as an optimization problem similar to the design of multidimensional digital filters
We considered the microphone array pruning problem combined with the design of sparse filters
Summary
Beamforming is a spatial filtering technique used to enhance the required signal via a sensor array for directional signal transmission or reception [1,2]. The beamformer design problem can be formulated as an optimization problem similar to the design of multidimensional digital filters Various optimization methods, such as linear programming techniques [5], quadratic programming techniques [6], and second-order cone programming [7] have been applied. The design of sparse beamforming filters is of great interest In solving this l0 -norm problem, an often subproblem is to employ a l1 -norm as a linear relaxation of the original problem, and iterate on the number of zeroes via a successive thinning technique [14,15]. We formulate and propose a novel algorithm for the sequential pruning of a microphone array, and at the same time tackle the sparse design of beamforming filters through successive thinning.
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