New article on the MoprheuS music generation system in IEEE Transactions of Affective Computng

I'm please to announce the latest article I wrote together with Prof. Elaine Chew on MorpheuS, published in IEEE Transactions on Affective Computing. The paper explains the inner workings of MorpheuS, a music generation system that is able to generate pieces with a fixed pattern structure and given tension.

Herremans D., Chew E.. 2017. MorpheuS: generating structured music with constrained patterns and tension. IEEE Transactions on Affective Computing. PP (In Press)(99)

Preprint version.

Abstract:
Automatic music generation systems have gained in popularity and sophistication as advances in cloud computing have enabled large-scale complex computations such as deep models and optimization algorithms on personal devices. Yet, they still face an important challenge, that of long-term structure, which is key to conveying a sense of musical coherence. We present the MorpheuS music generation system designed to tackle this problem. MorpheuS' novel framework has the ability to generate polyphonic pieces with a given tension profile and long- and short-term repeated pattern structures. A mathematical model for tonal tension quantifies the tension profile and state-of-the-art pattern detection algorithms extract repeated patterns in a template piece. An efficient optimization metaheuristic, variable neighborhood search, generates music by assigning pitches that best fit the prescribed tension profile to the template rhythm while hard constraining long-term structure through the detected patterns. This ability to generate affective music with specific tension profile and long-term structure is particularly useful in a game or film music context. Music generated by the MorpheuS system has been performed live in concerts.