A New Block-Based Stochastic Adaptive Algorithm for Sparse Echo Cancellation

摘要：

The sparse nature of a network echo response makes standard NLMS based adaptive algorithms perform poorly. Fast convergence, yet low complexity, of adaptive filter design causes another challenge. In this paper, a new Stochastic Selective Partial Update Normalized Least Mean Square (SSPNLMS) algorithm is proposed. Based on an efficient stochastic search and two block-based tap selection criteria, this algorithm exploits both sparseness of the echo response and sparseness of the input signal to achieve high quality adaptive filters without much computational cost. Simulation results show our proposed algorithm has promising convergence performance for the cases of white Gaussian noise input signal and the speech signals.

关键词： sparse echo cancellation adaptive filter stochastic search

作者: De-Sheng Chen Kui-Shun Chou Yi-Wen Wang

作者单位: Department of Computer Science and Information Engineering Feng-Chia University Taichung, Taiwan, R.O.C.

会议类型: 国际会议

会议名称: 2010 2nd International Conference on Signal Processing System(2010年信号处理系统国际会议 ICSPS 2010)

会议地点: 大连

会议语种:英文

页码: 756-760

在线出版日期: 2010-07-05（万方平台首次上网日期，不代表论文的发表时间）