This paper proposes a deconvolution-based network (DCNN) model for DOA estimation of direct source and early reflections under reverberant scenarios. Considering that the first-order reflections of the sound source also contain spatial directivity like the direct source, we treat both of them as the sources in the learning process. We use the covariance matrix of high order Ambisonics (HOA) signals in the time domain as the input feature of the network, which is concise while containing precise spatial information under reverberant scenarios. Besides, we use the deconvolution-based network for the spatial pseudo-spectrum (SPS) reconstruction in the 2D polar space, based on which the spatial relationship between elevation and azimuth can be depicted. We have carried out a series of experiments based on simulated and measured data under different reverberant scenarios, which prove the robustness and accuracy of the proposed DCNN model.

翻译：本文建议了一种基于分变网络的模型,用于在回旋情景下对直接源和早期反射进行数据分析。考虑到声源的第一阶反射还包含直接源的空间直接性,我们将两者视为学习过程中的来源。我们使用时间域中高压氨比松信号(HOA)的共变矩阵作为网络的输入特征,该矩阵简明扼要,在回动情景下包含精确的空间信息。此外,我们使用基于分变网络的2D极空间空间空间模拟光谱(SPS)重建空间假相(SPS)重建,在此基础上可以描述海拔和方位之间的空间关系。我们根据不同回动情景下的模拟和计量数据进行了一系列实验,证明了拟议的DCNN模型的稳健性和准确性。