Blind Separation Algorithm for Audio Signal Based on Genetic Algorithm and Neural Network

被引:1
作者
Li, Dahui [1 ,2 ]
Diao, Ming [1 ]
Dai, Xuefeng [2 ]
机构
[1] Harbin Engn Univ, Coll Informat & Commun Engn, Harbin 15001, Heilongjiang, Peoples R China
[2] Qiqihar Univ, Comp & Control Engn Inst, Qiqihar 161006, Peoples R China
来源
ISISE 2008: INTERNATIONAL SYMPOSIUM ON INFORMATION SCIENCE AND ENGINEERING, VOL 1 | 2008年
关键词
blind separation algorithms; genetic algorithms; neural networks;
D O I
10.1109/ISISE.2008.307
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 blind separation of audio signal is an important application of blind signal separation technology. The traditional separation algorithm based on neural network is analyzed first in this article. The shortage of it is easy to fall into local minimum, and it causes the limitation of convergence slowly and separation results inaccurate. Then, a separation algorithm is designed with genetic algorithm and neural network. The algorithm will optimize the initial value of the weight of separation, in order to control the sample by select operation, or cross operation, or mutation operation in the entire search space. Experiments show that it can obtain optimal values for separation matrix, and the speed of audio signal blind separation is quick and effective obviously.
引用
收藏
页码:436 / +
页数:3
相关论文
共 8 条
[1]   Natural gradient works efficiently in learning [J].
Amari, S .
NEURAL COMPUTATION, 1998, 10 (02) :251-276
[2]  
ARMRI S, 1997, SIGNAL PROCESS, V45, P2692
[3]  
ARMRI S, 1996, ADV NEURAL INFORM PR, V8, P757
[4]   AN INFORMATION MAXIMIZATION APPROACH TO BLIND SEPARATION AND BLIND DECONVOLUTION [J].
BELL, AJ ;
SEJNOWSKI, TJ .
NEURAL COMPUTATION, 1995, 7 (06) :1129-1159
[5]   Equivariant adaptive source separation [J].
Cardoso, JF ;
Laheld, BH .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 1996, 44 (12) :3017-3030
[6]   INDEPENDENT COMPONENT ANALYSIS, A NEW CONCEPT [J].
COMON, P .
SIGNAL PROCESSING, 1994, 36 (03) :287-314
[7]  
JUTTEN C, 1994, SIGNAL PROCESS, V24, P1
[8]   Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources [J].
Lee, TW ;
Girolami, M ;
Sejnowski, TJ .
NEURAL COMPUTATION, 1999, 11 (02) :417-441