Online Blind Speech Separation Using Time-Varying All-Pole Source Model

Independent vector analysis (IVA) is a state-of-the-art blind source separation (BSS) method that utilizes higher-order correlation between frequency components in each source. However, online-IVA assumes a spherical multivariate distribution and it is not flexible. This letter presents an online BSS method based on the time-varying all-pole (TVAP) source model, which can better capture the spectral envelope of the source signal. The cost function is formulated under the maximum likelihood criterion, where the demixing matrix is updated using iterative projection algorithm and the TVAP model parameters is optimized based on a fixed-point iteration framework. Furthermore, the TVAP model with a low-order all-pole filter effectively solves the permutation problem. Experimental results show that the proposed algorithm outperforms the online-IVA methods for both simulated and live-recorded datasets.