A salient characteristic of human perception of music is that musical events are perceived as being grouped temporally into structural units such as phrases or motifs. Segmentation of musical sequences into structural units is a topic of ongoing research, both in cognitive psychology and music information retrieval. Computational models of music segmentation are typically based either on explicit knowledge of music theory or human perception, or on statistical and information-theoretic properties of musical data. The former, rule-based approach has been found to better account for (human annotated) segment boundaries in music than probabilistic approaches [14], although the statistical model proposed in [14] performs almost as well as state-of-the-art rule-based approaches. In this paper, we propose a new probabilistic segmentation method, based on Restricted Boltzmann Machines (RBM). By sampling, we determine a probability distribution over a subset of visible units in the model, conditioned on a configuration of the remaining visible units. We apply this approach to an n-gram representation of melodies, where the RBM generates the conditional probability of a note given its (n-1) predecessors. We use this quantity in combination with a threshold to determine the location of segment boundaries. A comparative evaluation shows that this model slightly improves segmentation performance over the model proposed in [14], and as such is closer to the state-of-the-art rule-based models.