User-guided independent vector analysis with source activity tuning

In this paper, user-guided source separation based on independent vector analysis is presented. In this framework, temporal power variations of sources can be tuned by a user. The information is exploited as prior distributions of source activities in independent vector analysis with time-varying Gaussian model, and source signals are separated by maximum a posteriori (MAP) estimation. Experimental evaluations show the source activity tuning is much effective to improve the separation performance in hard mixing conditions such as long reverberation or level mismatch of sources.