We present three main contributions to single-channel source separation. Our first contribution is a group-sparsity inducing penalty specifically tailored for nonnegative matrix factorization (NMF) with the Itakura-Saito divergence: in many music tracks, there are whole intervals where at least one source is inactive. The group-sparsity penalty we propose allows identifying these intervals blindly and learn source specific dictionaries. As a consequence, those learned dictionaries can be used to do source separation in other parts of the track were several sources are active. These two tasks of identification and separation are performed simultaneously in one run of group-sparsity Itakura-Saito NMF.