A Hybrid System for Automatic Generation of Style-Specific Accompaniment

Creating distinctive harmonizations in an identifiable style may be one of the most difficult tasks for amateur song writers, a novel and acceptable melody being relatively easier to produce; and this difficulty may result in the abandonment of otherwise worthwhile projects. To model and assist in this creative process, we propose a hybrid system for generating style-specific accompaniment, which is capable of creating new harmonizations for melodies, with proper harmonic resolutions, in a style that is learned from only a few examples. In the proposed system, a chord tone determination module first learns, then determines, which notes in a given melody are likely chord tones. According to these chord tones, triads are assigned first to the bars with unambiguous solutions, and these triads serve as checkpoints. The system then constructs possible chord progressions using neo-Riemannian transforms between checkpoints, and represents the alternate paths in a tree structure. A Markov chain with learned probabilities for these neo-Riemanian transforms then generates the final chord progression. We select four songs by the British rock band, Radiohead, to evaluate the system. Three songs are used for training, and an accompaniment is generated for the held out melody. We present the results of two case studies. We find that the system generates chords closely related to the original, and the resulting chord transitions reinforce the phrase structure of the melody.