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Most soundfield synthesis approaches deal with extensive and regularloudspeaker arrays, which are often not suitable for home audio systems, due tophysical space constraints. In this article we propose a technique forsoundfield synthesis through more easily deployable irregular loudspeakerarrays, i.e. where the spacing between loudspeakers is not constant, based ondeep learning. The input are the driving signals obtained through a plane wavedecomposition-based technique. While the considered driving signals are able tocorrectly reproduce the soundfield with a regular array, they show degradedperformances when using irregular setups. Through a complex-valuedConvolutional Neural Network (CNN) we modify the driving signals in order tocompensate the errors in the reproduction of the desired soundfield. Since noground-truth driving signals are available for the compensated ones, we trainthe model by calculating the loss between the desired soundfield at a number ofcontrol points and the one obtained through the driving signals estimated bythe network. Numerical results show better reproduction accuracy with respectto the plane wave decomposition-based technique, pressure-matching approach andto linear optimizers for driving signal compensation.

Synthesis of Soundfields through Irregular Loudspeaker Arrays Based on Convolutional Neural Networks