  In this paper we introduce a novel feature augmentation approach for
generating structured musical compositions comprising melodies and harmonies.
The proposed method augments a connectionist generation model with count-down
to song conclusion and meter markers as extra input features to study whether
neural networks can learn to produce more aesthetically pleasing and structured
musical output as a consequence of augmenting the input data with structural
features. An RNN architecture with LSTM cells is trained on the Nottingham folk
music dataset in a supervised sequence learning setup, following a Music
Language Modelling approach, and then applied to generation of harmonies and
melodies. Our experiments show an improved prediction performance for both
types of annotation. The generated music was also subjectively evaluated using
an on-line Turing style listening test which confirms a substantial improvement
in the aesthetic quality and in the perceived structure of the music generated
using the temporal structure.
