  Signal separation and extraction are important tasks for devices recording
audio signals in real environments which, aside from the desired sources, often
contain several interfering sources such as background noise or concurrent
speakers. Blind Source Separation (BSS) provides a powerful approach to address
such problems. However, BSS algorithms typically treat all sources equally and
do not resolve uncertainty regarding the ordering of the separated signals at
the output of the algorithm, i.e., the outer permutation problem. This paper
addresses this problem by incorporating prior knowledge into the adaptation of
the demixing filters, e.g., the position of the sources, in a Bayesian
framework. We focus here on methods based on Independent Vector Analysis (IVA)
as it elegantly and successfully deals with the internal permutation problem.
By including a background model, i.e., a model for sources we are not
interested to separate, we enable the algorithm to extract the sources of
interest in overdetermined and underdetermined scenarios at a low computational
complexity. The proposed framework allows to incorporate prior knowledge about
the demixing filters in a generic way and unifies several known and newly
proposed algorithms using a Bayesian view. For all algorithmic variants, we
provide efficient update rules based on the iterative projection principle. The
performance of a large variety of representative algorithmic variants,
including very recent algorithms, is compared using measured room impulse
responses.
