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Summary: ROBUST VARIATIONAL SPEECH SEPARATION USING FEWER MICROPHONES THAN
SPEAKERS
Steven Rennie, Parham Aarabi, Trausti Kristjansson, Brendan J. Frey, Kannan Achan
Department of Electrical and Computer Engineering
University of Toronto
ABSTRACT
A variational inference algorithm for robust speech separa-
tion, capable of recovering the underlying speech sources
even in the case of more sources than microphone obser-
vations, is presented. The algorithm is based upon an gen-
erative probabilistic model that fuses time-delay of arrival
(TDOA) information with prior information about the speak-
ers and application, to produce an optimal estimate of the
underlying speech sources. Simulation results are presented
for the case of two, three and four underlying sources and
two microphones observations corrupted by noise. The re-
sulting SNR gains (24dB with two sources, 15dB with three
sources, and 9dB with four sources) are significantly higher
than previous speech separation techniques.
1. INTRODUCTION
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