Abstract: Many sounds that humans encounter are hierarchical in nature; a piano note is one of many played during a performance, which is one of many instruments in a band, which might be playing in a bar with other noises occurring. Inspired by this, we re-frame the musical source separation problem as hierarchical, combining similar instruments together at certain levels and separating them at other levels. This allows us to deconstruct the same mixture in multiple ways, depending on the appropriate level of the hierarchy for a given application. In this paper, we present various methods for hierarchical musical instrument separation, with some methods focusing on separating specific instruments (like guitars) and other methods that determine what to separate based on a user-supplied audio example. We additionally show that separating all hierarchy levels is possible even when training data is limited at fine-grained levels of the hierarchy.