论文标题
通过基于卷积神经网络的不规则扬声器阵列综合声场
Synthesis of Soundfields through Irregular Loudspeaker Arrays Based on Convolutional Neural Networks
论文作者
论文摘要
大多数Soundfield合成方法都涉及由于物理空间限制，通常不适合家庭音频系统的广泛和常规扬声器阵列。在本文中，我们通过更容易部署的不规则扬声器阵列提出了一种用于音轨合成的技术，即基于深度学习，扬声器之间的间距不是恒定的。输入是通过基于平面波分解技术获得的驱动信号。虽然被考虑的驾驶信号能够使用常规阵列正确复制声场，但使用不规则的设置时，它们显示出降解的性能。通过复杂的卷积神经网络（CNN），我们修改了驾驶信号，以补偿所需声场的繁殖错误。由于没有用于补偿的地面驾驶信号，因此我们通过在许多控制点和通过网络估计的驾驶信号获得的驱动点计算所需的声场之间的损失来训练模型。数值结果显示了基于平面波分解的技术，压力匹配方法以及对驱动信号补偿的线性优化器的繁殖精度更好。
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.
