The robustness of the Kalman filter to double talk and its rapid convergence make it a popular approach for addressing acoustic echo cancellation (AEC) challenges. However, the inability to model nonlinearity and the need to tune control parameters cast limitations on such adaptive filtering algorithms. In this paper, we integrate the frequency domain Kalman filter (FDKF) and deep neural networks (DNNs) into a hybrid method, called NeuralKalman, to leverage the advantages of deep learning and adaptive filtering algorithms. Specifically, we employ a DNN to estimate nonlinearly distorted far-end signals, a transition factor, and the nonlinear transition function in the state equation of the FDKF algorithm. Experimental results show that the proposed NeuralKalman improves the performance of FDKF significantly and outperforms strong baseline methods.

翻译：卡尔曼滤波对双端通话的鲁棒性及其快速收敛特性，使其成为解决声学回声消除（AEC）挑战的主流方法。然而，此类自适应滤波算法存在无法建模非线性及需手动调参的局限性。本文通过融合频域卡尔曼滤波（FDKF）与深度神经网络（DNN），提出了一种名为NeuralKalman的混合方法，以充分发挥深度学习与自适应滤波算法的优势。具体而言，我们采用DNN来估计非线性失真的远端信号、过渡因子以及FDKF算法状态方程中的非线性过渡函数。实验结果表明，NeuralKalman显著提升了FDKF的性能，并超越了强基线方法。