Geographic patterns of song variation are common in passerines and can develop as a consequence of the mechanisms of song acquisition and dispersal. In particular, the timing of dispersal relative to the sensory learning phase and the time of song crystallization is important. For example, when the sensory phase continues after dispersal or when males learn new songs every breeding season, i.e. open-ended learner, neighborhoods can develop where males share their songs. In this study, we utilize a unique, comprehensive dispersal and song recording dataset to investigate the existence and development of micro-geographic song variation in a wild passerine population. Machine learning song analysis methods allow us to overcome perceptual bias in the classification of the songs of New Zealand hihi (Notiomystis cincta). We show that males share more song elements of their repertoire with their neighbors than with more distant males or with males from the same natal area, implying that repertoire is acquired post-dispersal. Finally, we suggest that high levels of male competition have driven the development of post-dispersal vocal learning behavior in this species.