Application Studies on Voice Signal Blind Separation of Independent Component Analysis

被引：0

作者：

Zhang, Peng ^{[1
]}

Li, Wen-juan ^{[1
]}

Wang, Guo-hua ^{[1
]}

Chen, Hui-xian ^{[1
]}

Wang, Qi-ying ^{[1
]}

Li, Ceng ^{[2
]}

机构：

[1] Inst Xinxing Appl Technol, Hefei, Peoples R China

[2] Anhui ZHONG AO Inst Technol, Hefei, Peoples R China

来源：

2013 THIRD INTERNATIONAL CONFERENCE ON INSTRUMENTATION & MEASUREMENT, COMPUTER, COMMUNICATION AND CONTROL (IMCCC) | 2013年

关键词：

independent component analysis; voice signal; blind source analysis; FastICA;

D O I：

10.1109/IMCCC.2013.121

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The Independent Component Analysis (ICA) method has applied in the field of blind source separation. On the basis of analyzing ICA, the study ameliorates the FastICA. The conventional FastICA has only a second order convergence rate and combination of static model and batch optimization algorithm. An improved ICA algorithm is therefore proposed to reduce the iteration steps and dynamic the algorithm. The experimental results show that the improved algorithm achieved satisfactory results.

引用

页码：536 / 539

页数：4

共 5 条

[1] Robust neural networks with on-line learning for blind identification and blind separation of sources [J].