Under deter mined blind source separation algorithm based on normal vector of hyperplane

作者：Xiao, Ming^*; Xie, Sheng-Li; Fu, Yu-Li

来源：Acta Automatica Sinica, 2008, 34(2): 142-149.

DOI：10.3724/SP.J.1004.2008.00142

• Hyperplane clustering
• K-source interval
• Normal vector
• Sparse component analysis (SCA)

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 l1-norm solution. Several experiments demonstrate the performance of the proposed algorithm.

• 出版日期2008-2
• 单位华南理工大学