摘要
探讨欠定情况下(观测信号少于源数目)的盲信号分离.首先给出了m维超平面的法矢量的计算公式,提出了一个基于超平面法矢量的矩阵恢复算法.其次针对语音分离,提出了k源区间及其检测方法,从而使k-SCA条件下的算法推广到了非稀疏信号的盲分离.在源信号重建上,提出了一个简化l^1范数解的新算法.几个仿真实验(含语音信号实验)证实了所提出算法的性能.
Discussion of blind signal separation problem under underdetermined case （i.e., the case of less observed signals than sources） is presented. First, a formula to calculate the normal vector of any hyperplane is given and a mixing matrix recovery algorithm based on the normal vector of any hyperplane is proposed. Second, for audio signal, k-source intervals are introduced and a method to detect them is proposed. So, the algorithms under the k-SCA condition are extended to blind non-sparse signal separation. To reconstruct the sources, a new algorithm is proposed to simplify the l^1-norm solution. Several experiments demonstrate the performance of the proposed algorithm.
出处
《自动化学报》
EI
CSCD
北大核心
2008年第2期142-149,共8页
Acta Automatica Sinica
基金
国家自然科学基金(U0635001，60505005，60674033)
广东省自然科学基金(04205783，05006508)
科技部重大基础前期研究专项(2005CCA04100)资助~~
关键词
欠定盲信号分离(BSS)
稀疏成分分析(SCA)
超平面聚类
法矢量
k源区间
Underdetermined blind signal separation （BSS）, sparse component analysis （SCA）, hyperplane clustering, normal vector, k-source interval
