In this paper, a deep-learning-based method for sound field reconstruction is proposed. The possibility to reconstruct the magnitude of the sound pressure in the frequency band 30–300 Hz for an entire room by using a very low number of irregularly distributed microphones arbitrarily arranged is shown. Moreover, the approach is agnostic to the location of the measurements in the Euclidean space. In particular, the presented approach uses a limited number of arbitrary discrete measurements of the magnitude of the sound field pressure in order to extrapolate this field to a higher-resolution grid of discrete points in space with a low computational complexity. The method is based on a U-net-like neural network with partial convolutions trained solely on simulated data, which itself is constructed from numerical simulations of Green's function across thousands of common rectangular rooms. Although extensible to three dimensions and different room shapes, the method focuses on reconstructing the two-dimensional plane of a rectangular room from measurements of the three-dimensional sound field. Experiments using simulated data together with an experimental validation in a real listening room are shown. The results suggest a performance which may exceed conventional reconstruction techniques for a low number of microphones and computational requirements.

中文翻译：

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房间内的声场重建：喷绘满足超分辨率。

本文提出了一种基于深度学习的声场重构方法。显示了通过使用非常少量的任意分布的不规则分布的麦克风来重构整个房间的30–300 Hz频带中的声压幅度的可能性。而且，该方法与欧几里得空间中的测量位置无关。特别地，所提出的方法使用有限数量的声场压力的大小的任意离散测量，以便以低计算复杂度将该场外推到空间中离散点的高分辨率网格。该方法基于类似U-net的神经网络，其中部分卷积仅在模拟数据上训练，其本身是通过Green'的数值模拟构建的 在数千个普通矩形房间中起作用。尽管可以扩展到三个维度和不同的房间形状，但是该方法着重于根据三维声场的测量来重建矩形房间的二维平面。显示了在真实的聆听室中使用模拟数据和实验验证进行的实验。结果表明，对于低数量的麦克风和计算要求，其性能可能会超过传统的重建技术。显示了在真实的聆听室中使用模拟数据和实验验证进行的实验。结果表明，对于低数量的麦克风和计算要求，其性能可能会超过传统的重建技术。显示了在真实的聆听室中使用模拟数据和实验验证进行的实验。结果表明，对于低数量的麦克风和计算要求，其性能可能会超过传统的重建技术。