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

TL;DR

This paper introduces a deep learning-based method using complex-valued CNNs to improve soundfield synthesis with irregular loudspeaker arrays, enabling better reproduction accuracy in space-constrained environments.

Contribution

It presents a novel neural network approach to compensate for irregular loudspeaker array configurations, enhancing soundfield reproduction without requiring ground-truth driving signals.

Findings

Improved soundfield reproduction accuracy over traditional methods

Effective compensation for irregular loudspeaker array configurations

Numerical results demonstrate superior performance of the proposed CNN approach

Abstract

Most soundfield synthesis approaches deal with extensive and regular loudspeaker arrays, which are often not suitable for home audio systems, due to physical space constraints. In this article we propose a technique for soundfield synthesis through more easily deployable 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 complex-valued 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 reproduction accuracy with respect to the plane wave decomposition-based technique, pressure-matching approach and to linear optimizers for driving signal compensation.