Source Separation with a Sensor Array using Graphical Models and Subband Filtering
Hagai T. Attias · 2002
Source separation is an important problem at the intersection of several fields, including machine learning, signal processing, and speech technology. Here we describe new separation algorithms which are based on probabilistic graphical models with latent variables. In contrast with existing methods, these algorithms exploit detailed models to describe source properties. They also use subband filtering ideas to model the reverberant environment, and employ an explicit model for background and sensor noise. We leverage variational techniques to keep the computational complexity per EM iteration linear in the number of frames. 1 The Source Separation Problem Fig. 1 illustrates the problem of source separation with a sensor array. In this problem, signals from K independent sources are received by each of L ≥ K sensors. The task is to extract the sources from the sensor signals. It is a difficult task, partly because the received signals are distorted versions of the originals. There are two types of distortions.