Blind separation and sound localization by using frequency-domain ICA

被引：3
作者：
Azetsu, Tadahiro
Uchino, Eiji
Suetake, Noriaki
机构：
[1] Yamaguchi Univ, Dept Phys Biol & Informat, Yamaguchi 7538512, Japan
[2] Yamaguchi Prefectural Univ, Dept Environm Design, Yamaguchi 7538502, Japan
来源：
SOFT COMPUTING | 2007年 / 11卷 / 02期
关键词：
blind separation; independent component analysis; sound localization; frequency domain approach;
D O I：
10.1007/s00500-006-0076-4
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The independent component analysis (ICA) in the frequency domain is a method to deal with a blind signal separation problem in which propagation time delays are included in the mixing process of signals. We propose an extended method of the frequency-domain ICA accompanying the estimation of the relative propagation time delays and the propagation coefficient ratios. The effectiveness of the proposed method has been confirmed by simulation experiments. In addition, the sound localization by the proposed method is further discussed.
引用
收藏
页码：185 / 192
页数：8
相关论文
共 16 条
[1]  
Amari S, 1996, ADV NEUR IN, V8, P757
[2]   Natural gradient works efficiently in learning [J].
Amari, S .
NEURAL COMPUTATION, 1998, 10 (02) :251-276
[3]  
[Anonymous], 1998, PROC 1998 INT S NONL
[4]  
ARAKI S, 2002, P ICASSP, V2, P1785
[5]   Detection and separation of speech event using audio and video information fusion and its application to robust speech interface [J].
Asano, F ;
Yamamoto, K ;
Hara, I ;
Ogata, J ;
Yoshimura, T ;
Motomura, Y ;
Ichimura, N ;
Asoh, H .
EURASIP JOURNAL ON APPLIED SIGNAL PROCESSING, 2004, 2004 (11) :1727-1738
[6]   AN INFORMATION MAXIMIZATION APPROACH TO BLIND SEPARATION AND BLIND DECONVOLUTION [J].
BELL, AJ ;
SEJNOWSKI, TJ .
NEURAL COMPUTATION, 1995, 7 (06) :1129-1159
[7]   INDEPENDENT COMPONENT ANALYSIS, A NEW CONCEPT [J].
COMON, P .
SIGNAL PROCESSING, 1994, 36 (03) :287-314
[8]  
Hyvärinen A, 2001, INDEPENDENT COMPONENT ANALYSIS: PRINCIPLES AND PRACTICE, P71
[9]  
Kurita S., 1999, BLIND SIGNAL SEPARAT, P21
[10]   An approach to blind source separation based on temporal structure of speech signals [J].
Murata, N ;
Ikeda, S ;
Ziehe, A .
NEUROCOMPUTING, 2001, 41 :1-24
12