  Automatic generation of sequences has been a highly explored field in the
last years. In particular, natural language processing and automatic music
composition have gained importance due to the recent advances in machine
learning and Neural Networks with intrinsic memory mechanisms such as Recurrent
Neural Networks. This paper evaluates different types of memory mechanisms
(memory cells) and analyses their performance in the field of music
composition. The proposed approach considers music theory concepts such as
transposition, and uses data transformations (embeddings) to introduce semantic
meaning and improve the quality of the generated melodies. A set of
quantitative metrics is presented to evaluate the performance of the proposed
architecture automatically, measuring the tonality of the musical compositions.
