Speech Separation Using an Adaptive Sparse Dictionary Algorithm

We present a greedy adaptive algorithm that builds a sparse orthogonal dictionary from the observed data. In this paper, the algorithm is used to separate stereo speech signals, and the phase information that is inherent to the extracted atom pairs is used for clustering and identification of the original sources. The performance of the algorithm is compared to that of the adaptive stereo basis algorithm, when the sources are mixed in echoic and anechoic environments. We find that the algorithm correctly separates the sources, and can do this even with a relatively small number of atoms.