Imposing higher-level Structure in Polyphonic Music Generation using Convolutional Restricted Boltzmann Machines and Constraints

Since computers can automate such processes, automatic music generation has become a small, but steadily emerging field in Artificial Intelligence and Machine Learning. Nevertheless, automatic music generation as a problem is far from solved: musical outputs created by artificial systems are regarded as a curiosity by human listeners at best, but all too often they are taken as a direct offense to our sense of musical aesthetics. This sensitivity to violations of even the most subtle musical norms illustrates how complex the problem of (especially polyphonic) music generation is. In addition, there are hardly any objective evaluation criteria to rigorously test and compare music generation systems. This is lamentable, not least since successful methods for automatic music generation would be of considerable commercial interest to the music, gaming, and film industries.