  This paper addresses the issue of long-scale correlations that is
characteristic for symbolic music and is a challenge for modern generative
algorithms. It suggests a very simple workaround for this challenge, namely,
generation of a drum pattern that could be further used as a foundation for
melody generation. The paper presents a large dataset of drum patterns
alongside with corresponding melodies. It explores two possible methods for
drum pattern generation. Exploring a latent space of drum patterns one could
generate new drum patterns with a given music style. Finally, the paper
demonstrates that a simple artificial neural network could be trained to
generate melodies corresponding with these drum patters used as inputs.
Resulting system could be used for end-to-end generation of symbolic music with
song-like structure and higher long-scale correlations between the notes.
