Statistical Convergence Analysis for Optimal Control of DFT-Domain Adaptive Echo Canceler
Statistical Convergence Analysis for Optimal Control of DFT-Domain Adaptive Echo Canceler
复制标题
DFT域自适应回声消除器优化控制的统计收敛分析
DOI：
10.1109/taslp.2017.2671422
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Jun Yang
中科院分区：
文献类型：
--
作者：
Feiran Yang;Gerald Enzner;Jun Yang
频域自适应滤波器（FADF）由于其低复杂度和快速收敛速度而被广泛应用于回波抵消系统中。然而，具有固定步长的Festival算法在收敛速度、稳态失调、跟踪能力和对近端语音干扰的鲁棒性之间表现出折衷。针对这一问题，提出了几种变步长FNN算法。然而，国家的最先进的变步长FSTO算法没有处理这个问题全面。本文提出了一种新的鲁棒变步长控制方法的FANG算法。基于对Festival算法的统计分析，通过最小化每帧处的真实权重向量与估计权重向量之间的均方差（MSD）来导出每个频率点的最佳步长。步长的计算需要系统距离和观测噪声功率谱密度（PSD）。利用MSD的确定性递推方程估计系统距离，利用远端信号和误差信号之间的幅度平方相干函数计算噪声PSD。此外，一个密切的联系之间的建议FRENT和频域卡尔曼滤波器被揭示。具体地，这里提出的工作可以被理解为自适应地监视和控制卡尔曼滤波器的底层声学状态空间的手段，包括用于在突然的回声路径变化之后快速重新适应自适应滤波器的手段。仿真结果表明，该算法具有收敛速度快、稳态失调小等优点。此外，该算法对双端通话干扰具有鲁棒性，但不需要明确的双端通话检测器。
The frequency-domain adaptive filter (FDAF) is widely used in echo cancellation systems due to its low complexity and fast convergence rate. However, the FDAF algorithm with a fixed step size exhibits a tradeoff among the convergence rate, steady-state misalignment, tracking ability, and robustness to near-end speech interferences. Several variable step-size FDAF algorithms were presented to address this problem. However, the state-of-the-art variable step-size FDAF algorithms did not handle this problem comprehensively. This paper presents a new robust variable step-size control approach to the FDAF algorithm. Based on a statistical analysis of the FDAF algorithm, an optimal step size for each frequency bin is derived by minimizing the mean-square deviation (MSD) between the true weight vector and estimated weight vector at each frame. Calculation of the step size requires the system distance and the observation noise power spectral density (PSD). The system distance is estimated using the deterministic recursive equations of MSD and the noise PSD is computed using the magnitude squared coherence function between the far-end signal and error signal. Moreover, a close link between the proposed FDAF and the frequency-domain Kalman filter is revealed. Specifically, the work presented here can be understood as a means to adaptively monitor and control the underlying acoustic state space of the Kalman filter, including means for fast readaptation of the adaptive filter after abrupt echo path changes. Simulation results demonstrate that the proposed algorithm can achieve fast convergence and low steady-state misalignment. Furthermore, the algorithm is robust to the double-talk interferences, but it does not require an explicit double-talk detector.