  A new algorithm for dynamic independent vector extraction is proposed. It is
based on the mixing model where mixing parameters related to the
source-of-interest (SOI) are time-variant while the separating parameters are
time-invariant. A contrast function based on the quasi-likelihood approach is
optimized using the Newton-Raphson approach. The update is computed without
imposing the orthogonal constraint, and the orthogonality is enforced
afterward. This yields an algorithm that is significantly faster than
gradient-based algorithms while different from fixed-point methods, which are
even faster. We show advantageous properties of the proposed algorithm compared
to the fixed-point methods in an on-line processing regime where stable
convergence to the SOI is the important issue. The effectiveness of the method
is demonstrated in a speech extraction experiment with a dense microphone
array.
