  To train a machine learning model is necessary to take numerous decisions
about many options for each process involved, in the field of sequence
generation and more specifically of music composition, the nature of the
problem helps to narrow the options but at the same time, some other options
appear for specific challenges. This paper takes the framework proposed in a
previous research that did not consider rhythm to make a series of design
decisions, then, rhythm support is added to evaluate the performance of two RNN
memory cells in the creation of monophonic music. The model considers the
handling of music transposition and the framework evaluates the quality of the
generated pieces using automatic quantitative metrics based on geometry which
have rhythm support added as well.
