Performance Evaluation of Selected Adaptive Filters for Acoustic Echo Cancelation over Voice over Internet Protocol

Ibrahim O. Oyetunji, K. Amusa, I. A. Adejumobi, Amidu O. Mustapha
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Abstract

Advancement of technology has increased the value of the telecommunications industry. One of the services with the rapid growth is Voice over Internet Protocol (VoIP). However, echo is a common disturbing effect in VoIP calls. Instead of suppressing the presence of echo in the VoIP communication channel, echo cancellation works better in full-duplex mode. This paper compares performances of some selected adaptive filters for cancelation of acoustic echo in VoIP channel. The framework for adaptive echo cancellation is built around finite impulse response and fourth-order infinite impulse response Chebyshev type II filters for the modeling of echo and acoustic environment, respectively. Selected adaptive algorithms for analysis are least mean square, recursive least square and frequency domain adaptive filter. The analysis is carried out in MATLAB 2018a environment. Echo return loss enhancement, error estimate and convergence rate are the performance indices employed for performance comparison of selected adaptive algorithms. It was found that the FDAF algorithm performed best based on computed error return loss enhancement and error estimate figures. In addition, FDAF converges faster than LMS and RLS with average time of 0.026431 s over 2048 filter lengths. Thus, FDAF shows a better promise over the two variants of time domain adaptive algorithm (LMS and RLS) analyzed in this paper.
自适应滤波器在网络话音回波消除中的性能评价
技术的进步提高了电信业的价值。Internet协议语音(VoIP)是目前发展最快的业务之一。然而,在VoIP通话中,回声是一种常见的干扰效应。在全双工模式下,回声消除效果更好,而不是抑制VoIP通信信道中回声的存在。本文比较了几种自适应滤波器在VoIP信道中消除回声的性能。基于有限脉冲响应和四阶无限脉冲响应切比雪夫II型滤波器,建立了自适应回波抵消框架,分别对回波和声环境进行建模。选择了最小均方、递推最小二乘和频域自适应滤波算法进行分析。分析在MATLAB 2018a环境下进行。回波回波损耗增强、误差估计和收敛速度是所选自适应算法的性能比较指标。通过计算误差回波损耗增强和误差估计图,发现FDAF算法的性能最好。此外,FDAF在2048个滤波器长度下的平均收敛时间为0.026431 s,比LMS和RLS更快。因此,相对于本文分析的两种时域自适应算法(LMS和RLS), FDAF显示出更好的前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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