Room geometry inference algorithms rely on the localization of acoustic reflectors to identify boundary surfaces of an enclosure. Rooms with highly absorptive walls or walls at large distances from the measurement setup pose challenges for such algorithms. As it is not always possible to localize all walls, we present a data-driven method to jointly detect and localize acoustic reflectors that correspond to nearby and/or reflective walls. A multi-branch convolutional recurrent neural network is employed for this purpose. The network's input consists of a time-domain acoustic beamforming map, obtained via Radon transform from multi-channel room impulse responses. A modified loss function is proposed that forces the network to pay more attention to walls that can be estimated with a small error. Simulation results show that the proposed method can detect nearby and/or reflective walls and improve the localization performance for the detected walls.

翻译：房间几何形状推断算法依赖于声学反射器的定位来识别封闭空间的边界表面。高度吸声的墙壁或与测量装置距离较远的墙壁给此类算法带来了挑战。由于并非所有墙壁都能被定位，我们提出了一种数据驱动方法，用于联合检测和定位对应附近和/或高反射性墙壁的声学反射器。为此，采用了一个多分支卷积递归神经网络。网络的输入是通过对多通道房间脉冲响应进行Radon变换获得的时域声学波束成形图。我们提出了一种改进的损失函数，迫使网络更加关注那些能以较小误差估计的墙壁。仿真结果表明，所提方法能够检测附近和/或高反射性墙壁，并提升对检测到的墙壁的定位性能。