UTCS Artificial Intelligence
Creating Melodies with Evolving Recurrent Networks
Active from 2000 - 2001
Music composition is a domain well-suited for evolutionary reinforcement learning. It is possible to write down rules for good melodies, and those rules can be used as a fitness function. Our initial results shows that the networks learn to express primitive rules on tonality and rhythm this way. Furthermore, by including rules for the style of a particular composer (e.g. Bela Bartok) the output melodies locally resemble the composer's work. Several interesting structures are discovered: for instance, the concept of transposition is naturally adopted by the model to reproduce familiar patterns with slight variations.
Automatic Music Composition using Genetic Algorithm and Neural Networks: A Constrained Evolution Approach
Chun-Chi Chen, Technical Report HR-00-02, Department of Computer Sciences, The University of Texas at Austin.