This paper describes a greedy algorithm for audio source separation of repeated musical patterns. The problem is understood as retrieving from a set of mixtures the part that is redundant among them and the parts that are specific to only one mixture. The key assumption is the sparsity of all the sources in the same multiscale dictionary. Synthetic and real life examples of source separation of hand cut repeated musical patterns are exposed. Results shows that the proposed method succeeds in simultaneously providing a sparse approximant of the mixtures and a separation of the sources.