Many music composition algorithms attempt to compose music in a particular style. The resulting music is often impressive and indistinguishable from the style of the training data, but it tends to lack significant innovation. In an effort to increase innovation in the selection of pitches and rhythms, we present a system that discovers musical motifs by coupling machine learning techniques with an inspirational component. Unlike many generative models, the inspirational component allows the composition process to originate outside of what is learned from the training data. Candidate motifs are extracted from non-musical media such as images and audio. Machine learning algorithms select and return the motifs that most resemble the training data. This process is validated by running it on actual music scores and testing how closely the discovered motifs match the expected motifs. We examine the information content of the discovered motifs by comparing the entropy of the discovered motifs, candidate motifs, and training data. We measure innovation by comparing the probability of the training data and the probability of the discovered motifs given the model.