Beamforming results depend on the spatial resolution of the microphone array used, which may lead to sources close to each other being considered as one. Deconvolution methods that consider all directions simultaneously, such as DAMAS, produce better results in these situations. However, they have a high computational cost, often lack sufficient speed to be used in real-time applications, and have limited accuracy at lower frequencies. This paper introduces a hybrid method to perform deconvolution using a neural network that can improve the speed of deconvolution on high-resolution grids by more than 2 orders of magnitude, while also generating sparser maps without sacrificing accuracy compared to the compressed DAMAS method.
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