Compositional Steering of Music Transformers

Musical composition is a combinatorial art where composers extend sequences by choosing from a vast set of possible feature combinations that yield the compositions their distinctive qualities. Increasingly, composers are using generative models, such as music transformers, for crafting their pieces. Unfortunately, for composers to “steer” these models to satisfy their qualitative features typically requires retraining (which can be prohibitively expensive); further, existing models are unable to deal with arbitrary combinations of features at scale. In this paper we build on lightweight fine-tuning methods, such as prefix tuning and bias tuning, to propose a novel contrastive loss that enables us to steer music transformers over arbitrary combinations of logical features, with a relatively small number of extra parameters. We provide both quantitative and qualitative evaluations of our method which demonstrate its efficacy with respect to existing methods, as well as a case-study where our method was used to compose long-form musical pieces. Musical examples are available for listening online. 1