MorpheuS: generating structured music with constrained patterns and tension
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.
Cite
@article{arxiv.1812.04832,
title = {MorpheuS: generating structured music with constrained patterns and tension},
author = {Dorien Herremans and Elaine Chew},
journal= {arXiv preprint arXiv:1812.04832},
year = {2018}
}
Comments
IEEE Transactions on Affective Computing. PP(99)