2 research outputs found
Independent Vector Analysis with more Microphones than Sources
We extend frequency-domain blind source separation based on independent
vector analysis to the case where there are more microphones than sources. The
signal is modelled as non-Gaussian sources in a Gaussian background. The
proposed algorithm is based on a parametrization of the demixing matrix
decreasing the number of parameters to estimate. Furthermore, orthogonal
constraints between the signal and background subspaces are imposed to
regularize the separation. The problem can then be posed as a constrained
likelihood maximization. We propose efficient alternating updates guaranteed to
converge to a stationary point of the cost function. The performance of the
algorithm is assessed on simulated signals. We find that the separation
performance is on par with that of the conventional determined algorithm at a
fraction of the computational cost.Comment: Accepted to WASPAA 2019, 5 pages, 3 figure
MM Algorithms for Joint Independent Subspace Analysis with Application to Blind Single and Multi-Source Extraction
In this work, we propose efficient algorithms for joint independent subspace
analysis (JISA), an extension of independent component analysis that deals with
parallel mixtures, where not all the components are independent. We derive an
algorithmic framework for JISA based on the majorization-minimization (MM)
optimization technique (JISA-MM). We use a well-known inequality for
super-Gaussian sources to derive a surrogate function of the negative
log-likelihood of the observed data. The minimization of this surrogate
function leads to a variant of the hybrid exact-approximate diagonalization
problem, but where multiple demixing vectors are grouped together. In the
spirit of auxiliary function based independent vector analysis (AuxIVA), we
propose several updates that can be applied alternately to one, or jointly to
two, groups of demixing vectors.
Recently, blind extraction of one or more sources has gained interest as a
reasonable way of exploiting larger microphone arrays to achieve better
separation. In particular, several MM algorithms have been proposed for
overdetermined IVA (OverIVA). By applying JISA-MM, we are not only able to
rederive these in a general manner, but also find several new algorithms. We
run extensive numerical experiments to evaluate their performance, and compare
it to that of full separation with AuxIVA. We find that algorithms using
pairwise updates of two sources, or of one source and the background have the
fastest convergence, and are able to separate target sources quickly and
precisely from the background. In addition, we characterize the performance of
all algorithms under a large number of noise, reverberation, and background
mismatch conditions.Comment: 15 pages, 4 figure