Single Channel Signal Separation Using Maximum Likelihood Subspace Projection

Abstract

This paper presents a technique for extracting multiple source signals when only a single channel observation is available. The proposed separation algorithm is based on a subspace decomposition. The observation is projected onto subspaces of interest with different sets of basis functions, and the original sources are obtained by weighted sums of the projections. A flexible model for density estimation allows an accurate modeling of the distributions of the source signals in the subspaces, and we develop a filtering technique using a maximum likelihood (ML) approach to match the observed single channel data with the decomposition. Our experimental results show good separation performance on simulated mixtures of two music signals as well as two voice signals.