摘要
将Kullback-Leibler散度和反馈机制引入到非负矩阵分解(NMF),得到一种新的盲源分离算法(KLFNMF)。该算法利用KL散度度量非负矩阵分解效果,并利用本次分离出的源信号与混合信号之间的相关系数,衡量所分离源信号的纯度;每次分解后,提取出最纯的那个分离信号,并形成新的混合信号,从而逐个得到所有的源信号。仿真结果表明,本算法分离性能优于基于欧氏距离的NMF盲源分离算法。
By introducing the Kullback - Leibler divergence and feedback mechanism into nonnegative matrix factorization （NMF）, a new blind source separation algorithm （KL- NMF） is presented. The KL divergence is used to measure the effects of nonnegative matrix factorization, and the correlation coefficient between the separated signal and the mixed signal is used to measure the purity of the separated signals. After each factorization, the most pure signal is extracted and a new mixed signal is obtained. Thus, all source signals are separated one by one. The simulation results show that the separation performance of the proposed algorithm is superior to the algorithm Euclidean distance based NMF.
出处
《电声技术》
2015年第3期73-77,共5页
Audio Engineering
基金
国防科技重点实验室基金项目(9140C131010109DZ46)
