论文标题
声学回声取消的协同卡尔曼和深层后滤波方法
A Synergistic Kalman- and Deep Postfiltering Approach to Acoustic Echo Cancellation
论文作者
论文摘要
我们介绍了一种协同方法，以将自适应Kalman滤波与深度神经网络的后过滤器相结合的双对词稳健的声学回声取消。所提出的算法克服了以突然回声路径变化为特征的场景中基于卡尔曼滤波器的适应控制的众所周知的局限性。作为关键创新，我们建议利用干扰信号成分的不同统计特性，以稳健地估计适应步骤大小。这是通过利用后过滤器的近端估计值和卡尔曼过滤器的估计误差来实现的。所提出的协同方案允许在突然回声路径变化后自适应过滤器快速重新分配，而不会损害静态场景中最新方法实现的稳态性能。
We introduce a synergistic approach to double-talk robust acoustic echo cancellation combining adaptive Kalman filtering with a deep neural network-based postfilter. The proposed algorithm overcomes the well-known limitations of Kalman filter-based adaptation control in scenarios characterized by abrupt echo path changes. As the key innovation, we suggest to exploit the different statistical properties of the interfering signal components for robustly estimating the adaptation step size. This is achieved by leveraging the postfilter near-end estimate and the estimation error of the Kalman filter. The proposed synergistic scheme allows for rapid reconvergence of the adaptive filter after abrupt echo path changes without compromising the steady state performance achieved by state-of-the-art approaches in static scenarios.
