A subspace approach to single channel signal separation using maximum likelihood weighting filters

The goal of this work is to extract multiple source signals when only a single channel observation is available. We propose a new signal separation algorithm based on a subspace decomposition. The observation is transformed into subspaces of interest with different sets of basis functions. 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.