  Dereverberation of a moving speech source in the presence of other
directional interferers, is a harder problem than that of stationary source and
interference cancellation. We explore joint multi channel linear prediction
(MCLP) and relative transfer function (RTF) formulation in a stochastic
framework and maximum likelihood estimation. We found that the combination of
spatial filtering with distortion-less response constraint, and time-varying
complex Gaussian model for the desired source signal at a reference microphone
does provide better signal estimation. For a stationary source, we consider
batch estimation, and obtain an iterative solution. Extending to a moving
source, we formulate a linear time-varying dynamic system model for the MCLP
coefficients and RTF based online adaptive spatial filter. For the case of
tracking a desired source in the presence of interfering sources, the same
formulation is used by specifying the RTF. Simulated experimental results show
that the proposed scheme provides better spatial selectivity and
dereverberation than the traditional methods, for both stationary and dynamic
sources even in the presence of interfering sources.
