自适应回声对消的初期迭代统计学模型及改进算法

为减少滤波器长度,提出自适应滤波算法初期迭代统计学模型及改进的回声消除算法。提出的统计学模型分析了自适应算法迭代初期滤波器各系数的均值和方差。基于该模型提出的改进算法,先检测回声路径峰值,进而确定回声路径延时,然后以延时为中心,用一个短的滤波器辨识原回声路径活跃系数部分。用实际回声路径进行仿真,理论和实验结果均表明,新算法在迭代的前75～100步已可准确检测回声路径峰值并确定延时;而减少滤波器长度,可大幅提高自适应算法收敛速度并降低算法计算复杂度。

A STATISTIC MODEL FOR EARLY ADAPTIVE ITERATION AND A MODIFIED ALGORITHM OF ECHO CANCELLATION

To decrease the length of the adaptive filter,a statistic model is proposed for adaptive algorithm in its early iterations,as well as a novel algorithm for echo cancellation.The statistic model analyzes the expectation and variance of each coefficient of filter in the early iterations of adaptive algorithm.The modified algorithm based on the model identifies the location of the peak of the echo path and estimate the bulk delay.After the estimation,a shorter adaptive filter centered about the peak coefficient is used to approach only the active coefficients instead of the whole echo path. Simulations with real echo path and theory show that the peak coefficient is discriminated and the estimation of delay can be obtained in the early 75～100 iterations.A short filter is used to identify the echo path,thus increasing the convergence speed and reducing the computational complexity.