基于最小体积约束的频域卷积盲源分离

Frequency-domain Convolutive Blind Source Separation with Minimum Volume Constraint

频域盲源分离算法多数基于窄带假设,该假设在长混响环境下不成立。基于卷积传递函数(Convolutive Transfer Function,CTF)的多通道非负矩阵分解(Multichannel Nonnegative Matrix Factorization,MNMF)方法不依赖窄带假设,在长混响环境下的分离性能较其他传统算法有显著提升。但是,非负矩阵分解(NMF)对源信号功率谱进行近似估计在大多数情况下是病态的,其最优解不唯一。本文提出了一种基于最小体积约束的频域卷积盲源分离方法,在多通道非负矩阵分解(CTF-MNMF)的目标函数中,引入NMF基矩阵的最小体积约束来提高问题的适定性和求解参数的可辨识性。采用Majorization-Minimization(MM)优化方法对最小体积约束的目标函数进行求解,导出了估计参数的闭式解。仿真实验表明,在长混响环境下,所提方法比CTF-MNMF具有更好的分离性能。

Most frequency-domain blind source separation algorithms are based on the narrow-band assumption,which is no longer applicable in the long reverberation environment.The convolutive transfer function-based multichannel nonnegative matrix factorization(CTF-MNMF)does not rely on the narrow-band assumption,and the separation performance in the long reverberation environment is significantly improved compared with other traditional methods.However,the nonnegative matrix factorization(NMF)approximates the power spectrum of source signal,which is ill-posed in most cases,and the optimal solution is non-unique.In this paper,a frequency-domain blind source separation method with minimum volume constraint is proposed.Minimum volume constraint of the NMF basis matrix is added into the objective function of CTF-MNMF,aiming at improve the fitness of the problem and the discriminability of the estimated parameters.Majorization-Minimization(MM)optimization method is used to solve the objective function with the minimum volume constraint,and the closed-form solution of the estimated parameters is derived.Simulation experiments show that the separation performance of the method is significantly improved compared with the CTF-MNMF method in a long reverberation environment.