An auxiliary-function approach to online independent vector analysis for real-time blind source separation

This paper proposes online independent vector analysis (IVA) based on an auxiliary-function approach for real-time blind speech separation. A batch auxiliary-function approach is naturally extended with autoregressive approximation of an auxiliary variable. Experimental evaluations show that the proposed online algorithm works in real time and attains relatively high signal-to-interference ratios without environment-sensitive tuning parameters such as step size under both spatially stationary and dynamic conditions compared to usual real-time IVAs using natural gradient updates or block-wise updates. Our implementation of the proposed algorithm works in real-time for four-channel observations on PCs and worked stably over 7 hours in realistic noisy environments.