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The wavelet transform-domain adaptive filter for nonlinear acoustic echo cancellation

机译:非线性声学回声消除的小波变换域自适应滤波器

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The usage of low-quality components in communicating devices introduces acoustic non-linearity. The presence of nonlinearity creates challenges in noise cancellation applications, especially the acoustic echo cancellation (AEC) that requires an adaptive filter of a very high order. However, the functional link adaptive filter (FLAF) algorithm models the acoustic nonlinearity efficiently but shows slow convergence performance due to a very high filter order. To improve the convergence performance of the FLAF, the wavelet transform-domain FLAF (WTD-FLAF) is proposed for nonlinear AEC (NAEC) applications. The convergence rate is improved by decomposing a higher-order adaptive filter into smaller-order subfilters. The convergence speed improvement is gained at the expense of increased computational complexity. A low complexity version of the WTD-FLAF, named as selective update WTD-FLAF (SU-WTD-FLAF) algorithm, is also presented. The SU-WTD-FLAF algorithm is based on the selective coefficient update approach. Computer simulations demonstrate that the convergence performance of the proposed algorithms outperforms the standard FLAF.
机译:在通信设备中使用低质量组件引入了声学非线性。非线性存在在噪声消除应用中产生挑战,特别是需要非常高阶的自适应滤波器的声学回声消除(AEC)。然而,功能链路自适应滤波器(FLAF)算法有效地模拟了声学非线性,但由于非常高的过滤器顺序,较慢的收敛性能。为了提高FLAF的收敛性能,为非线性AEC(NAEC)应用提出了小波变换域FLAF(WTD-FLAF)。通过将更高阶的自适应滤波器分解成较小的子滤光器,改善了收敛速率。收敛速度改善以增加计算复杂性的代价。还提出了WTD-FLAF的低复杂性版本,名称为选择性更新WTD-FLAF(SU-WTD-FLAF)算法。 SU-WTD-FLAF算法基于选择性系数更新方法。计算机仿真表明,所提出的算法的收敛性能优于标准的FLAF。

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