Estimating a time-varying spatial covariance matrix for a beamforming algorithm is a challenging task, especially for wearable devices, as the algorithm must compensate for time-varying signal statistics due to rapid pose-changes. In this paper, we propose Neural Integrated Covariance Estimators for Beamformers, NICE-Beam. NICE-Beam is a general technique for learning how to estimate time-varying spatial covariance matrices, which we apply to joint speech enhancement and dereverberation. It is based on training a neural network module to non-linearly track and leverage scene information across time. We integrate our solution into a beamforming pipeline, which enables simple training, faster than real-time inference, and a variety of test-time adaptation options. We evaluate the proposed model against a suite of baselines in scenes with both stationary and moving microphones. Our results show that the proposed method can outperform a hand-tuned estimator, despite the hand-tuned estimator using oracle source separation knowledge.

翻译：为波形演算法估计一个时间变化的空间共变矩阵是一项具有挑战性的任务,特别是对可磨损的设备而言,因为算法必须补偿由于迅速变化而变化的时间变化信号统计数据。在本文中,我们提议为波形变形者、NICE-Beam 提供神经综合共变模拟器。NICE-Baam是学习如何估计时间变化空间共变矩阵的一种一般技术,我们适用于联合语音增强和变换。它基于对神经网络模块进行非线性跟踪和利用场景信息的训练。我们将我们的解决方案整合到一个波形化管道中,这样可以进行简单的培训,比实时推断更快,以及各种测试-时间适应选项。我们用固定麦克风和移动麦克风的场景中一组基线对拟议模型进行评估。我们的结果显示,拟议方法可以超越手调的估测仪,尽管使用手调的估测器或控制源分解知识。