Blind source separation coping with the change of the number of sources

被引:0
作者
Ito, Masanori [1 ]
Ohnishi, Noboru [1 ]
Mansour, Ali [2 ]
Kawamoto, Mitsuru [3 ,4 ]
机构
[1] Nagoya Univ, Grad Sch Informat Sci, Chikusa Ku, Nagoya, Aichi 4648603, Japan
[2] ENSIETA, Lab E3I2, F-29806 Brest 09, France
[3] Adv Ind Sci & Technol, Tsukuba, Ibaraki 3058568, Japan
[4] RIKEN, Biomimetic Control Res Ctr, Nagoya, Aichi 4630003, Japan
来源
NEURAL INFORMATION PROCESSING, PART II | 2008年 / 4985卷
关键词
blind source separation; time-variant system; dynamical instantaneous mixtures; independent component analysis;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This manuscript deals with the blind source separation problem with an instantaneous but dynamical mixture model. This study is limited to the case when the number of sources is time-variant. Theoretically, when new sources are detected, a new separating matrix should be estimated in order to extract all sources. However this effort implies an overwhelm computational cost. Our idea consists to use the previous separating matrix which was estimated before the appearance of the new sources. Owing to this point, the computational time and cost can be effectively reduced compared with the conventional separation scheme. Our new algorithm was corroborated with many simulations. Some results are given in the manuscript. The obtained and presented results clearly show that the proposed method outperformed the conventional method in processing time as well as in separation quality.
引用
收藏
页码:509 / +
页数:3
相关论文
共 50 条
  • [31] Majorization-minimization for blind source separation of sparse sources
    Mourad, Nasser
    Reilly, James P.
    Kirubarajan, T.
    SIGNAL PROCESSING, 2017, 131 : 120 - 133
  • [32] Handling a Dynamic Mixture of Sources in Blind Source Separation Tasks
    Phon-Amnuaisuk, Somnuk
    2013 CONFERENCE ON TECHNOLOGIES AND APPLICATIONS OF ARTIFICIAL INTELLIGENCE (TAAI), 2013, : 211 - 216
  • [33] Research on Influence of Source Number Estimation on Application of Blind Source Separation Algorithms
    Wang Chuanchuan
    Xu Jiaqi
    Zeng Yonghu
    ADVANCES IN INFORMATION AND COMMUNICATION TECHNOLOGY, 2017, 107 : 379 - 384
  • [34] Complex Blind Source Separation
    Kemiha, Mina
    Kacha, Abdellah
    CIRCUITS SYSTEMS AND SIGNAL PROCESSING, 2017, 36 (11) : 4670 - 4687
  • [35] Superefficiency in blind source separation
    Amari, S
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 1999, 47 (04) : 936 - 944
  • [36] Online Adaptive Quasi-Maximum Likelihood Blind Source Separation of Stationary Sources
    Weiss, Amir
    Yeredor, Arie
    2018 IEEE INTERNATIONAL CONFERENCE ON THE SCIENCE OF ELECTRICAL ENGINEERING IN ISRAEL (ICSEE), 2018,
  • [37] Blind source separation of more sources than mixtures using sparse mixture models
    Shi, ZW
    Tang, HW
    Tang, YY
    PATTERN RECOGNITION LETTERS, 2005, 26 (16) : 2491 - 2499
  • [38] Effective blind separation of skewed sources
    Martin-Clemente, Ruben
    Hornillo-Mellado, Susana
    SIGNAL PROCESSING, 2006, 86 (10) : 3085 - 3088
  • [39] Source Number Estimation Algorithm of FECG Based on Sparse Blind Source Separation Analysis
    Tan, Beihai
    Lin, Jinrong
    Peng, Qiuming
    Li, Weijun
    FOURTH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND INFORMATION SCIENCES (CCAIS 2015), 2015, : 132 - 136
  • [40] MACHINE VIBRATION MONITORING BY BLIND SOURCE SEPARATION AND CHANGE DETECTION
    Popescu, Theodor D.
    NEURAL NETWORK WORLD, 2009, 19 (03) : 263 - 277