The significance-aware EPFES to estimate a memoryless preprocessor for nonlinear acoustic echo cancellation
The significance-aware EPFES to estimate a memoryless preprocessor for nonlinear acoustic echo cancellation
复制标题
用于估计非线性声学回声消除的无记忆预处理器的重要性感知 EPFES
DOI：
10.1109/globalsip.2014.7032179
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Walter Kellermann
中科院分区：
文献类型：
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作者：
Christian Huemmer;Christian Hofmann;R. Maas;Walter Kellermann
本文提出了一种新的基于粒子滤波的非线性回声抵消无记忆预处理器系数估计方法。回声路径由线性有限脉冲响应滤波器(由归一化最小均方算法估计)之前的无记忆预处理器(以对扬声器的非线性进行建模)的非线性-线性级联来建模。为了识别扬声器信号的失真，我们遵循重要性感知滤波的概念，将无记忆预处理器的时变系数和房间脉冲响应向量的直接路径部分建模为具有非高斯概率分布的一个状态向量。针对状态向量与观测值之间的非线性关系，提出了一种基于进化策略的精英粒子滤波算法(EPFES)，该算法基于长期适应度来评估状态向量的实现情况，具有较高的计算效率。实验验证包括预定义的扬声器信号失真以及来自商业智能手机的真实录音。与著名的Hammerstein群NL-AEC模型相比，在两种情况下都降低了计算复杂度，并提高了可实现的系统辨识。
In this article, we introduce a novel approach for estimating the coefficients of a memoryless preprocessor for nonlinear acoustic echo cancellation (NL-AEC) using particle filtering. The acoustic echo path is modeled by a nonlinear-linear cascade of a memoryless preprocessor (to model the loudspeaker nonlinearities) preceding a linear finite impulse response filter (estimated by the normalized least mean square algorithm). For identifying the loudspeaker signal distortions, we follow the concept of significance-aware filtering by modeling the time-variant coefficients of the memoryless preprocessor and the direct-path part of the room impulse response vector as one state vector with non-Gaussian probability distribution. Due to the nonlinear relation between the state vector and the observation, we propose a computationally-efficient realization of the recently published elitist particle filter based on evolutionary strategies (EPFES), which evaluates realizations of the state vector based on long-term fitness measures. The experimental validation comprises predefined loudspeaker signal distortions as well as real recordings stemming from a commercial smartphone. In comparison to the well-known Hammerstein group model for NL-AEC, the computational complexity is reduced and the achievable system identification is improved for both scenarios.