Dimension reduction techniques such as Nonnegative Tensor Factorization are now classical for both source separation and estimation of multiple fundamental frequencies in audio mixtures. Still, few studies jointly addressed these tasks so far, mainly because separation is often based on the Short Term Fourier Transform (STFT) whereas recent music analysis algorithms are rather based on the Constant-Q Transform (CQT). The CQT is practical for pitch estimation because a pitch shift amounts to a translation of the CQT representation, whereas it produces a scaling of the STFT. Conversely, no simple inversion of the CQT was available until recently, preventing it from being used for source separation. Benefiting from advances both in the inversion of the CQT and in statistical modeling, we show how recent techniques designed for music analysis can also be used for source separation with encouraging results, thus opening the path to many crossovers between separation and analysis.