Vocal source separation and fundamental frequency estimation in music are tightly related tasks. The outputs of vocal source separation systems have previously been used as inputs to vocal fundamental frequency estimation systems; conversely, vocal fundamental frequency has been used as side information to improve vocal source separation. In this paper, we propose several different approaches for jointly separating vocals and estimating fundamental frequency. We show that joint learning is advantageous for these tasks, and that a stacked architecture which first performs vocal separation outperforms the other configurations considered. Furthermore, the best joint model achieves state-of-the-art results for vocal-f0 estimation on the iKala dataset. Finally, we highlight the importance of performing polyphonic, rather than monophonic vocal-f0 estimation for many real-world cases.