Kannan Govind, Milani Ali A, Panahi Issa M S, Kehtarnavaz Nasser
Electrical Engineering Department, The University of Texas at Dallas, TX, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:4327-30. doi: 10.1109/IEMBS.2010.5626192.
Active Noise Control (ANC) of fMRI acoustic noise using the conventional Filtered-X LMS (FXLMS) approach results in poor cancelation performance and slow convergence due to its broadband nature and the need for high order adaptive filters. High order adaptive filters are needed to effectively model the long acoustic impulse responses. Existing methods to improve the performance of FXLMS based broadband ANC systems are either computationally expensive or need elaborate implementation. In this paper we show a practical method to enhance the performance of FXLMS based algorithms, by deriving a crude estimate of the causalWiener filter and initializing the adaptive filter with the estimated Wiener filter. We observe that very fast convergence to the global minimum can be achieved along with huge gains in the noise cancelation performance. We call this method Wiener initialized FXLMS (WI-FXLMS).We show the effectiveness of the proposed approach for the active noise control of functional MRI acoustic noise and several other realistic noise sources.
使用传统的滤波-X最小均方（FXLMS）方法对功能磁共振成像（fMRI）的声学噪声进行有源噪声控制，由于其宽带特性以及对高阶自适应滤波器的需求，导致抵消性能较差且收敛速度缓慢。需要高阶自适应滤波器来有效地对长声学脉冲响应进行建模。现有的提高基于FXLMS的宽带有源噪声控制系统性能的方法要么计算成本高昂，要么需要复杂的实现。在本文中，我们展示了一种实用方法，通过推导因果维纳滤波器的粗略估计并用估计的维纳滤波器初始化自适应滤波器，来提高基于FXLMS的算法的性能。我们观察到，可以实现非常快速地收敛到全局最小值，同时在噪声抵消性能方面有巨大提升。我们将这种方法称为维纳初始化FXLMS（WI-FXLMS）。我们展示了所提出的方法对于功能磁共振成像声学噪声以及其他几种实际噪声源的有源噪声控制的有效性。
