Compensation of Driving Signals for Soundfield Synthesis through Irregular Loudspeaker Arrays Based on Convolutional Neural Networks

In this article we propose a technique for soundfield synthesis for irregular loudspeaker arrays, i.e. where the spacing between loudspeakers is not constant, based on deep learning. The input are the driving signals obtained through a plane wave decomposition-based technique. While the considered driving signals are able to correctly reproduce the soundfield with a regular array, they show degraded performances when using irregular setups. Through a Convolutional Neural Network (CNN) we modify the driving signals in order to compensate the errors in the reproduction of the desired soundfield. Since no ground-truth driving signals are available for the compensated ones, we train the model by calculating the loss between the desired soundfield at a number of control points and the one obtained through the driving signals estimated by the network. Numerical results show better performances both with respect to the plane wave decomposition-based technique and the pressure-matching approach.