An online algorithm for blind source separation with Gaussian mixture model
Masashi Ohata, T. Tokunari, Kiyotoshi Matsuoka · 2002
This paper proposes a new online algorithm for blind source separation. It is based on the maximum likelihood estimation of the mixing matrix and the parameterized probability density functions of the sources. For the model of each source signal a Gaussian mixture model is adopted. When one attempts to devise an online algorithm in this framework, two problems arise. First, what kind of recursive minimization is efficient from a computational point of view? Second, how can the singularity of the likelihood function associated with the mixture model be avoided? Same techniques for solving these problems are described.