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To train a machine learning model is necessary to take numerous decisionsabout many options for each process involved, in the field of sequencegeneration and more specifically of music composition, the nature of theproblem helps to narrow the options but at the same time, some other optionsappear for specific challenges. This paper takes the framework proposed in aprevious research that did not consider rhythm to make a series of designdecisions, then, rhythm support is added to evaluate the performance of two RNNmemory cells in the creation of monophonic music. The model considers thehandling of music transposition and the framework evaluates the quality of thegenerated pieces using automatic quantitative metrics based on geometry whichhave rhythm support added as well.

A framework to compare music generative models using automatic evaluation metrics extended to rhythm