Audio source separation with a single sensor

Authors:
L. Benaroya;F. Bimbot;R. Gribonval
Affiliations:
IRISA, CNRS & INRIA, Rennes, France;-;-
Venue:
IEEE Transactions on Audio, Speech, and Language Processing
Year:
2006

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Abstract

In this paper, we address the problem of audio source separation with one single sensor, using a statistical model of the sources. The approach is based on a learning step from samples of each source separately, during which we train Gaussian scaled mixture models (GSMM). During the separation step, we derive maximum a posteriori (MAP) and/or posterior mean (PM) estimates of the sources, given the observed audio mixture (Bayesian framework). From the experimental point of view, we test and evaluate the method on real audio examples.