  In this paper, we introduce new methods and discuss results of text-based
LSTM (Long Short-Term Memory) networks for automatic music composition. The
proposed network is designed to learn relationships within text documents that
represent chord progressions and drum tracks in two case studies. In the
experiments, word-RNNs (Recurrent Neural Networks) show good results for both
cases, while character-based RNNs (char-RNNs) only succeed to learn chord
progressions. The proposed system can be used for fully automatic composition
or as semi-automatic systems that help humans to compose music by controlling a
diversity parameter of the model.
