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首页> 外文期刊>Chinese Journal of Electronics >An Adaptive Time-Domain Kalman Filtering Approach to Acoustic Feedback Cancellation for Hearing Aids
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An Adaptive Time-Domain Kalman Filtering Approach to Acoustic Feedback Cancellation for Hearing Aids

机译：用于助听器的声学反馈取消的自适应时域卡尔曼滤波方法

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摘要

The adaptive filtering approach has been widely used for acoustic feedback control in the hearing aids due to its excellent performance. The commonly used adaptive filtering algorithms employ a fixed step-size, which has to compromise between the initial convergence and the steady-state misalignment. Many variable stepsize adaptive algorithms have been proposed to handle this problem. In this paper, we propose a broadband Kalman filter to resolve this problem. The acoustic feedback path is modelled by a first-order Markov model, and the observation equation is constructed using more past data vector. A major issue in the hearing aids is the computational complexity. We thus present a simplified version to reduce the complexity, which bridges between the exact Kalman filter and the affine projection algorithm. The estimation of the process and measurement noise variance is discussed in detail. A two-feedback path model is adopted to improve the algorithm's lack of re-convergence. Simulation results confirm the proposed algorithm clearly outperforms the other variable step-size adaptive filtering approaches.
机译：自适应滤波方法由于其优异的性能而广泛用于助听器中的声反馈控制。常用的自适应滤波算法采用固定的阶梯大小，在初始收敛和稳态错位之间必须损害。已经提出了许多可变步骤STAPTIVE算法来处理此问题。在本文中，我们提出了一个宽带卡尔曼滤波器来解决这个问题。声反馈路径由一阶马尔可夫模型建模，并且使用更多过去的数据向量构建观察方程。助听器中的一个主要问题是计算复杂性。因此，我们提出了一种简化的版本来降低复杂性，在精确的卡尔曼滤波器和仿射投影算法之间桥接。详细讨论了处理和测量噪声方差的估计。采用双反馈路径模型来提高算法缺乏重新收敛。仿真结果证实了所提出的算法显然优于其他可变步长自适应滤波方法。

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