3 research outputs found
A Dynamic Algorithm for Blind Separation of Convolutive Sound Mixtures
We study an efficient dynamic blind source separation algorithm of
convolutive sound mixtures based on updating statistical information in the
frequency domain, andminimizing the support of time domain demixing filters by
a weighted least square method. The permutation and scaling indeterminacies of
separation, and concatenations of signals in adjacent time frames are resolved
with optimization of norm on cross-correlation
coefficients at multiple time lags. The algorithm is a direct method without
iterations, and is adaptive to the environment. Computations on recorded and
synthetic mixtures of speech and music signals show excellent performance.Comment: 22 pages, 9 figure
A Geometric Blind Source Separation Method Based on Facet Component Analysis
Given a set of mixtures, blind source separation attempts to retrieve the
source signals without or with very little information of the the mixing
process. We present a geometric approach for blind separation of nonnegative
linear mixtures termed {\em facet component analysis} (FCA). The approach is
based on facet identification of the underlying cone structure of the data.
Earlier works focus on recovering the cone by locating its vertices (vertex
component analysis or VCA) based on a mutual sparsity condition which requires
each source signal to possess a stand-alone peak in its spectrum. We formulate
alternative conditions so that enough data points fall on the facets of a cone
instead of accumulating around the vertices. To find a regime of unique
solvability, we make use of both geometric and density properties of the data
points, and develop an efficient facet identification method by combining data
classification and linear regression. For noisy data, we show that denoising
methods may be employed, such as the total variation technique in imaging
processing, and principle component analysis. We show computational results on
nuclear magnetic resonance spectroscopic data to substantiate our method
A Dynamic Algorithm for Blind Separation of Convolutive Sound Mixtures
We study an efficient dynamic blind source separation algorithm of convolutive sound mixtures based on updating statistical information in the frequency domain, and minimizing the support of time domain demixing filters by a weighted least square method. The permutation and scaling indeterminacies of separation, and concatenations of signals in adjacent time frames are resolved with optimization of l 1 Γl β norm on cross-correlation coefficients at multiple time lags. The algorithm is a direct method without iterations, and is adaptive to the environment. Computations on recorded and synthetic mixtures of speech and music signals show excellent performance