  In this paper we propose a method for separation of moving sound sources. The
method is based on first tracking the sources and then estimation of source
spectrograms using multichannel non-negative matrix factorization (NMF) and
extracting the sources from the mixture by single-channel Wiener filtering. We
propose a novel multichannel NMF model with time-varying mixing of the sources
denoted by spatial covariance matrices (SCM) and provide update equations for
optimizing model parameters minimizing squared Frobenius norm. The SCMs of the
model are obtained based on estimated directions of arrival of tracked sources
at each time frame. The evaluation is based on established objective separation
criteria and using real recordings of two and three simultaneous moving sound
sources. The compared methods include conventional beamforming and ideal ratio
mask separation. The proposed method is shown to exceed the separation quality
of other evaluated blind approaches according to all measured quantities.
Additionally, we evaluate the method's susceptibility towards tracking errors
by comparing the separation quality achieved using annotated ground truth
source trajectories.
