A Mixing Matrix Estimation Algorithm for the Time-Delayed Mixing Model of the Underdetermined Blind Source Separation Problem

Abstract

Considering the time-delayed mixing model of the underdetermined blind source separation problem, we propose a novel mixing matrix estimation algorithm in this paper. First, we introduce the short-time Fourier transform (STFT) to transform the mixed signals from the time domain to the time–frequency domain. Second, a neoteric transformation matrix is addressed to construct the linear clustering property of STFT coefficients. Then, a preeminent detection algorithm is raised to identify the single source points. After eliminating the low-energy points and outliers in the time–frequency domain, a potential function of clustering approach is put forward to cluster the single source points and obtain the clustering centers. Finally, the mixing matrix can be estimated through the derivation and calculation. The experimental results validate that the proposed algorithm not only accurately estimates the mixing matrix for the time-delayed mixing model of the underdetermined blind source separation problem but also has certain universality for different array structures. Therefore, both the effectiveness and superiority of the proposed algorithm have been verified.